Update README.md

feat: Add full documentation and standalone inference script to model card

This commit significantly improves the model card to make the custom model usable by others. The previous README was a default template.

**Key Changes:**

1. **Completed All Sections**: Filled in the `Model description` and `Intended uses & limitations` sections with specifics about the multi-head architecture and its purpose in disaster response.
2. **Added "How to Use" Section**: This is the most critical addition. It contains a complete, self-contained Python script that successfully loads the model and runs inference.
3. **In-line Documentation**: The script is segmented into logical blocks (Architecture, Labels, Setup, Prediction) with clear documentation explaining why each part is necessary. This is crucial for a custom model that cannot use the standard Hugging Face `pipeline`.
4. **Added Sample Output**: A raw JSON output block is included to show users the exact format of the prediction results.
5. **Cleaned Up Metadata**: Minor formatting improvements to the training hyperparameter list.

README.md

@@ -2,32 +2,47 @@
 library_name: transformers
 base_model: cardiffnlp/twitter-xlm-roberta-base-sentiment
 tags:
-- generated_from_trainer
-- sentiment
-- requests
-- disaster
-- response
+- text-classification
+- multi-label-classification
+- multi-head-classification
+- disaster-response
+- humanitarian-aid
+- social-media
 - twitter
-- x
-- social
-- media
+- generated_from_trainer
 model-index:
 - name: xlm-roberta-sentiment-requests
-  results: []
+  results:
+  - task:
+      type: text-classification
+    dataset:
+      name: community-datasets/disaster_response_messages
+      type: community-datasets
+      config: default
+      split: evaluation
+    metrics:
+    - name: F1 Micro
+      type: f1
+      value: 0.7240
+    - name: F1 Macro
+      type: f1
+      value: 0.3505
+    - name: Subset Accuracy
+      type: accuracy
+      value: 0.2588
 datasets:
 - community-datasets/disaster_response_messages
 pipeline_tag: text-classification
 language:
-- multilingual
 - en
+- multilingual
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+<!-- This model card has been generated automatically and then completed by a human. -->
 
 # xlm-roberta-sentiment-requests
 
-This model is a fine-tuned version of [cardiffnlp/twitter-xlm-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment) on the community-datasets/disaster_response_messages dataset.
+This model is a fine-tuned version of [cardiffnlp/twitter-xlm-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment) on the `community-datasets/disaster_response_messages` dataset. It has been adapted into a powerful **multi-head classification model** designed to analyze messages from social media during disaster events.
 It achieves the following results on the evaluation set:
 - Loss: 0.1465
 - F1 Micro: 0.7240
@@ -36,18 +51,221 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+This model uses a shared `XLM-RoBERTa` base to encode input text. The resulting text representation is then fed into two separate, independent classification layers (heads):
+*   A **Sentiment Head** with 3 outputs for `positive`, `neutral`, and `negative` classes.
+*   A **Multi-Label Head** with 41 outputs, which are decoded to predict the presence or absence of 37 different disaster-related categories.
+
+This dual-head architecture allows for a nuanced understanding of a message, capturing both its emotional content and its specific, actionable information.
 
 ## Intended uses & limitations
 
-More information needed
+This model is intended for organizations and researchers involved in humanitarian aid and disaster response. Potential applications include:
+*   **Automated Triage**: Quickly sorting through thousands of social media messages to identify the most urgent requests for help.
+*   **Situational Awareness**: Building a real-time map of needs by aggregating categorized messages.
+*   **Resource Allocation**: Directing resources more effectively by understanding the specific types of aid being requested.
+
+**Important**: Due to its custom architecture, this model **cannot** be used with the standard `pipeline("text-classification")` function. Please see the usage code below for the correct implementation.
+
+### How to Use
+This model requires custom code to handle its two-headed output. The following is a complete, self-contained Python script to run inference. You will need to have `transformers`, `torch`, and `safetensors` installed (`pip install transformers torch safetensors`).
+
+The script is broken into logical blocks:
+
+1.  **Model Architecture**: A Python class that defines the model's structure. This blueprint is required to load the saved weights.
+2.  **Label Definitions**: A "decoder ring" of functions to translate the model's numerical outputs into human-readable labels.
+3.  **Setup & Loading**: A function that handles all the one-time setup.
+4.  **Prediction Function**: The core logic that takes text and produces a dictionary of predictions.
+5.  **Main Execution**: An example of how to run the script.
+
+By copying the codes below from 1 to 5, you will be able to run the entire inference pipeline with all outputs.
+
+***
+1. **Model Architecture**: We define the necessary imports and the model architecture.
+```python
+import torch
+from torch import nn
+from transformers import AutoTokenizer, AutoConfig, AutoModel, PreTrainedModel
+from huggingface_hub import hf_hub_download
+from typing import Dict, Any
+from safetensors.torch import load_file
+
+class MultiHeadClassificationModel(PreTrainedModel):
+    def __init__(self, config, **kwargs):
+        super().__init__(config)
+        num_multilabels = kwargs.get("num_multilabels")
+        if num_multilabels is None:
+            raise ValueError("`num_multilabels` must be provided to initialize the model.")
+        self.backbone = AutoModel.from_config(config)
+        self.sentiment_classifier = nn.Linear(config.hidden_size, config.num_sentiment_labels)
+        self.multilabel_classifier = nn.Linear(config.hidden_size, num_multilabels)
+        self.init_weights()
+
+    def forward(self, input_ids=None, attention_mask=None, **kwargs):
+        outputs = self.backbone(input_ids, attention_mask=attention_mask, **kwargs)
+        cls_token_output = outputs.last_hidden_state[:, 0, :]
+        sentiment_logits = self.sentiment_classifier(cls_token_output)
+        multilabel_logits = self.multilabel_classifier(cls_token_output)
+        return {"sentiment_logits": sentiment_logits, "multilabel_logits": multilabel_logits}
+```
+***
+2.  **Label Definitions**: We embed the label definitions, which are essential for interpreting the model's output.
+```python
+def get_all_labels() -> Dict[str, Dict[int, str]]:
+    return {
+        'sentiment': get_sentiment_labels(), 'genre': get_genre_labels(), 'related': get_related_labels(),
+        'request': get_request_labels(), 'offer': get_offer_labels(), 'aid_related': get_aid_related_labels(),
+        'medical_help': get_medical_help_labels(), 'medical_products': get_medical_products_labels(),
+        'search_and_rescue': get_search_and_rescue_labels(), 'security': get_security_labels(),
+        'military': get_military_labels(), 'child_alone': get_child_alone_labels(), 'water': get_water_labels(),
+        'food': get_food_labels(), 'shelter': get_shelter_labels(), 'clothing': get_clothing_labels(),
+        'money': get_money_labels(), 'missing_people': get_missing_people_labels(),
+        'refugees': get_refugees_labels(), 'death': get_death_labels(), 'other_aid': get_other_aid_labels(),
+        'infrastructure_related': get_infrastructure_related_labels(), 'transport': get_transport_labels(),
+        'buildings': get_buildings_labels(), 'electricity': get_electricity_labels(), 'tools': get_tools_labels(),
+        'hospitals': get_hospitals_labels(), 'shops': get_shops_labels(), 'aid_centers': get_aid_centers_labels(),
+        'other_infrastructure': get_other_infrastructure_labels(), 'weather_related': get_weather_related_labels(),
+        'floods': get_floods_labels(), 'storm': get_storm_labels(), 'fire': get_fire_labels(),
+        'earthquake': get_earthquake_labels(), 'cold': get_cold_labels(), 'other_weather': get_other_weather_labels(),
+        'direct_report': get_direct_report_labels(),
+    }
+def get_genre_labels() -> Dict[int, str]: return {0: 'direct', 1: 'news', 2: 'social'}
+def get_related_labels() -> Dict[int, str]: return {0: 'no', 1: 'yes', 2: 'maybe'}
+def get_request_labels() -> Dict[int, str]: return {0: 'no', 1: 'yes'}
+def get_offer_labels() -> Dict[int, str]: return {0: 'no', 1: 'yes'}
+def get_aid_related_labels() -> Dict[int, str]: return {0: 'no', 1: 'yes'}
+def get_medical_help_labels() -> Dict[int, str]: return {0: 'no', 1: 'yes'}
+def get_medical_products_labels() -> Dict[int, str]: return {0: 'no', 1: 'yes'}
+def get_search_and_rescue_labels() -> Dict[int, str]: return {0: 'no', 1: 'yes'}
+def get_security_labels() -> Dict[int, str]: return {0: 'no', 1: 'yes'}
+def get_military_labels() -> Dict[int, str]: return {0: 'no', 1: 'yes'}
+def get_child_alone_labels() -> Dict[int, str]: return {0: 'no', 1: 'yes'}
+def get_water_labels() -> Dict[int, str]: return {0: 'no', 1: 'yes'}
+def get_food_labels() -> Dict[int, str]: return {0: 'no', 1: 'yes'}
+def get_shelter_labels() -> Dict[int, str]: return {0: 'no', 1: 'yes'}
+def get_clothing_labels() -> Dict[int, str]: return {0: 'no', 1: 'yes'}
+def get_money_labels() -> Dict[int, str]: return {0: 'no', 1: 'yes'}
+def get_missing_people_labels() -> Dict[int, str]: return {0: 'no', 1: 'yes'}
+def get_refugees_labels() -> Dict[int, str]: return {0: 'no', 1: 'yes'}
+def get_death_labels() -> Dict[int, str]: return {0: 'no', 1: 'yes'}
+def get_other_aid_labels() -> Dict[int, str]: return {0: 'no', 1: 'yes'}
+def get_infrastructure_related_labels() -> Dict[int, str]: return {0: 'no', 1: 'yes'}
+def get_transport_labels() -> Dict[int, str]: return {0: 'no', 1: 'yes'}
+def get_buildings_labels() -> Dict[int, str]: return {0: 'no', 1: 'yes'}
+def get_electricity_labels() -> Dict[int, str]: return {0: 'no', 1: 'yes'}
+def get_tools_labels() -> Dict[int, str]: return {0: 'no', 1: 'yes'}
+def get_hospitals_labels() -> Dict[int, str]: return {0: 'no', 1: 'yes'}
+def get_shops_labels() -> Dict[int, str]: return {0: 'no', 1: 'yes'}
+def get_aid_centers_labels() -> Dict[int, str]: return {0: 'no', 1: 'yes'}
+def get_other_infrastructure_labels() -> Dict[int, str]: return {0: 'no', 1: 'yes'}
+def get_weather_related_labels() -> Dict[int, str]: return {0: 'no', 1: 'yes'}
+def get_floods_labels() -> Dict[int, str]: return {0: 'no', 1: 'yes'}
+def get_storm_labels() -> Dict[int, str]: return {0: 'no', 1: 'yes'}
+def get_fire_labels() -> Dict[int, str]: return {0: 'no', 1: 'yes'}
+def get_earthquake_labels() -> Dict[int, str]: return {0: 'no', 1: 'yes'}
+def get_cold_labels() -> Dict[int, str]: return {0: 'no', 1: 'yes'}
+def get_other_weather_labels() -> Dict[int, str]: return {0: 'no', 1: 'yes'}
+def get_direct_report_labels() -> Dict[int, str]: return {0: 'no', 1: 'yes'}
+def get_sentiment_labels() -> Dict[int, str]: return {0: 'negative', 1: 'neutral', 2: 'positive'}
+```
+***
+3.  **Setup & Loading**: This setup function handles loading all components and reconstructing the necessary metadata.
+```python
+def load_essentials():
+    hub_repo_id = "spencercdz/xlm-roberta-sentiment-requests"
+    subfolder = "final_model"
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    all_labels_map = get_all_labels()
+    binary_tasks = [k for k, v in all_labels_map.items() if len(v) == 2 and k not in ['related', 'sentiment']]
+    multiclass_tasks = {k: len(v) for k, v in all_labels_map.items() if len(v) > 2}
+    
+    column_names = [f"{t}_{i}" for t, n in multiclass_tasks.items() for i in range(n)] + binary_tasks
+    multilabel_column_names = sorted(column_names)
+    num_multilabels = len(multilabel_column_names)
+    num_sentiment_labels = len(get_sentiment_labels())
+
+    tokenizer = AutoTokenizer.from_pretrained(hub_repo_id, subfolder=subfolder)
+    config = AutoConfig.from_pretrained(hub_repo_id, subfolder=subfolder)
+    config.num_sentiment_labels = num_sentiment_labels
+
+    model_shell = MultiHeadClassificationModel(config=config, num_multilabels=num_multilabels)
+    weights_path = hf_hub_download(repo_id=hub_repo_id, filename="model.safetensors", subfolder=subfolder)
+    state_dict = load_file(weights_path, device=device)
+    model_shell.load_state_dict(state_dict, strict=False)
+    model = model_shell.to(device)
+    model.eval()
+
+    metadata = {
+        "binary_tasks": binary_tasks, "multiclass_tasks": multiclass_tasks,
+        "multilabel_column_names": multilabel_column_names,
+        "all_labels": all_labels_map, "device": device
+    }
+    return model, tokenizer, metadata
+```
+***
+4.  **Prediction Function**: The prediction function takes the loaded components and input text to produce a decoded dictionary.
+```python
+def predict(text: str, model, tokenizer, metadata: Dict) -> Dict[str, Any]:
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512).to(metadata['device'])
+    with torch.no_grad():
+        outputs = model(**inputs)
+    
+    sentiment_probs = torch.softmax(outputs['sentiment_logits'], dim=-1).cpu().numpy()
+    multilabel_probs = torch.sigmoid(outputs['multilabel_logits']).cpu().numpy()
+
+    results = {}
+    sentiment_decoder = metadata['all_labels']['sentiment']
+    sentiment_pred_idx = sentiment_probs.argmax()
+    results['sentiment'] = {'prediction': sentiment_decoder.get(sentiment_pred_idx, "unknown"), 'confidence': sentiment_probs[0, sentiment_pred_idx].item()}
+    
+    for task_name in metadata['binary_tasks']:
+        idx = metadata['multilabel_column_names'].index(task_name)
+        prob = multilabel_probs[0, idx]
+        pred = 1 if prob > 0.5 else 0
+        results[task_name] = {'prediction': metadata['all_labels'][task_name][pred], 'confidence': (prob if pred == 1 else 1 - prob).item()}
+
+    for task_name, num_classes in metadata['multiclass_tasks'].items():
+        start_idx = metadata['multilabel_column_names'].index(f"{task_name}_0")
+        task_probs = multilabel_probs[0, start_idx : start_idx + num_classes]
+        pred_idx = task_probs.argmax()
+        results[task_name] = {'prediction': metadata['all_labels'][task_name].get(pred_idx, "unknown"), 'confidence': task_probs[pred_idx].item()}
+
+    return results
+```
+***
+5.  **Main Execution**: The main execution block shows how to use the functions and print the raw JSON output.
+```python
+if __name__ == "__main__":
+    model, tokenizer, metadata = load_essentials()
+    input_text = "I need food, water, and shelter. Help me! People are dying. We need more items."
+    
+    print(f"\n--- Predicting for Input ---\n\"{input_text}\"")
+    
+    predictions = predict(input_text, model, tokenizer, metadata)
+    
+    # Print the raw dictionary output
+    print("\n--- RAW DICTIONARY OUTPUT ---")
+    print(predictions)
+```
+
+### Sample Output
+```
+{'sentiment': {'prediction': 'negative', 'confidence': 0.999014139175415}, 'request': {'prediction': 'yes', 'confidence': 0.9999805688858032}, 'offer': {'prediction': 'no', 'confidence': 0.9995545148849487}, 'aid_related': {'prediction': 'yes', 'confidence': 0.9995179176330566}, 'medical_help': {'prediction': 'no', 'confidence': 0.9931818246841431}, 'medical_products': {'prediction': 'no', 'confidence': 0.9975765943527222}, 'search_and_rescue': {'prediction': 'no', 'confidence': 0.9981554746627808}, 'security': {'prediction': 'no', 'confidence': 0.999071478843689}, 'military': {'prediction': 'no', 'confidence': 0.9981452226638794}, 'child_alone': {'prediction': 'no', 'confidence': 0.9998688697814941}, 'water': {'prediction': 'yes', 'confidence': 0.9991873502731323}, 'food': {'prediction': 'yes', 'confidence': 0.9998394250869751}, 'shelter': {'prediction': 'yes', 'confidence': 0.9997198581695557}, 'clothing': {'prediction': 'no', 'confidence': 0.9982467889785767}, 'money': {'prediction': 'no', 'confidence': 0.9985392093658447}, 'missing_people': {'prediction': 'no', 'confidence': 0.998404324054718}, 'refugees': {'prediction': 'no', 'confidence': 0.9981242418289185}, 'death': {'prediction': 'yes', 'confidence': 0.9850122332572937}, 'other_aid': {'prediction': 'no', 'confidence': 0.9654157757759094}, 'infrastructure_related': {'prediction': 'no', 'confidence': 0.984534740447998}, 'transport': {'prediction': 'no', 'confidence': 0.9972304105758667}, 'buildings': {'prediction': 'no', 'confidence': 0.9881182312965393}, 'electricity': {'prediction': 'no', 'confidence': 0.9988776445388794}, 'tools': {'prediction': 'no', 'confidence': 0.9995874166488647}, 'hospitals': {'prediction': 'no', 'confidence': 0.999099850654602}, 'shops': {'prediction': 'no', 'confidence': 0.9996023178100586}, 'aid_centers': {'prediction': 'no', 'confidence': 0.9981774091720581}, 'other_infrastructure': {'prediction': 'no', 'confidence': 0.9968826770782471}, 'weather_related': {'prediction': 'no', 'confidence': 0.9632836580276489}, 'floods': {'prediction': 'no', 'confidence': 0.9960920810699463}, 'storm': {'prediction': 'no', 'confidence': 0.9963870048522949}, 'fire': {'prediction': 'no', 'confidence': 0.9993714094161987}, 'earthquake': {'prediction': 'no', 'confidence': 0.99778151512146}, 'cold': {'prediction': 'no', 'confidence': 0.9991660118103027}, 'other_weather': {'prediction': 'no', 'confidence': 0.9974269866943359}, 'direct_report': {'prediction': 'yes', 'confidence': 0.9763266444206238}, 'genre': {'prediction': 'direct', 'confidence': 0.9912198185920715}, 'related': {'prediction': 'yes', 'confidence': 0.9997092485427856}}
+```
 
 ## Training and evaluation data
 
-More information needed
+This model was fine-tuned on the `community-datasets/disaster_response_messages` dataset, which contains over 26,000 messages from real disaster events. Each message is labeled with 37 different categories, such as `aid_related` and `weather_related`, as well as the message `genre` (direct, news, social). The `sentiment` labels were added programmatically for the purpose of this multi-task training.
+
+The dataset was split into:
+*   Training set: ~21,000 samples
+*   Validation set: ~2,600 samples
+*   Test set: ~2,600 samples
 
 ## Training procedure
 
+The model was trained using the `transformers.Trainer` with a custom `MultiHeadClassificationModel` architecture. The training process optimized a combined loss from both the sentiment and multi-label classification heads. The best model was selected based on the `F1 Micro` score on the validation set.
+
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
@@ -57,608 +275,42 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
 - lr_scheduler_type: linear
-- num_epochs: 1000
+- num_epochs: 1000 (early stopping patience of 50 epochs)
 - mixed_precision_training: Native AMP
 
 ### Training results
 
-| Training Loss | Epoch | Step   | Validation Loss | F1 Micro | F1 Macro | Subset Accuracy |
-|:-------------:|:-----:|:------:|:---------------:|:--------:|:--------:|:---------------:|
-| 0.4267        | 1.0   | 658    | 0.2727          | 0.4953   | 0.0722   | 0.1053          |
-| 0.2662        | 2.0   | 1316   | 0.2291          | 0.5446   | 0.0906   | 0.1123          |
-| 0.2366        | 3.0   | 1974   | 0.2143          | 0.5682   | 0.1031   | 0.1279          |
-| 0.2234        | 4.0   | 2632   | 0.2058          | 0.5878   | 0.1160   | 0.1333          |
-| 0.2156        | 5.0   | 3290   | 0.1997          | 0.6022   | 0.1255   | 0.1380          |
-| 0.2094        | 6.0   | 3948   | 0.1949          | 0.6116   | 0.1311   | 0.1438          |
-| 0.2047        | 7.0   | 4606   | 0.1911          | 0.6189   | 0.1372   | 0.1438          |
-| 0.201         | 8.0   | 5264   | 0.1879          | 0.6226   | 0.1415   | 0.1446          |
-| 0.1974        | 9.0   | 5922   | 0.1852          | 0.6283   | 0.1494   | 0.1500          |
-| 0.1953        | 10.0  | 6580   | 0.1829          | 0.6357   | 0.1595   | 0.1524          |
-| 0.1926        | 11.0  | 7238   | 0.1808          | 0.6397   | 0.1677   | 0.1593          |
-| 0.1909        | 12.0  | 7896   | 0.1791          | 0.6419   | 0.1716   | 0.1609          |
-| 0.189         | 13.0  | 8554   | 0.1775          | 0.6459   | 0.1789   | 0.1656          |
-| 0.1877        | 14.0  | 9212   | 0.1762          | 0.6496   | 0.1864   | 0.1675          |
-| 0.1859        | 15.0  | 9870   | 0.1749          | 0.6531   | 0.1920   | 0.1667          |
-| 0.185         | 16.0  | 10528  | 0.1737          | 0.6550   | 0.1974   | 0.1737          |
-| 0.1838        | 17.0  | 11186  | 0.1727          | 0.6580   | 0.2019   | 0.1784          |
-| 0.1824        | 18.0  | 11844  | 0.1719          | 0.6602   | 0.2055   | 0.1749          |
-| 0.1816        | 19.0  | 12502  | 0.1709          | 0.6615   | 0.2089   | 0.1866          |
-| 0.1809        | 20.0  | 13160  | 0.1702          | 0.6630   | 0.2127   | 0.1823          |
-| 0.1799        | 21.0  | 13818  | 0.1695          | 0.6657   | 0.2173   | 0.1877          |
-| 0.1798        | 22.0  | 14476  | 0.1687          | 0.6675   | 0.2215   | 0.1904          |
-| 0.1788        | 23.0  | 15134  | 0.1681          | 0.6690   | 0.2228   | 0.1916          |
-| 0.1778        | 24.0  | 15792  | 0.1677          | 0.6706   | 0.2282   | 0.1904          |
-| 0.1773        | 25.0  | 16450  | 0.1670          | 0.6714   | 0.2305   | 0.1955          |
-| 0.1768        | 26.0  | 17108  | 0.1665          | 0.6727   | 0.2327   | 0.1967          |
-| 0.1766        | 27.0  | 17766  | 0.1660          | 0.6749   | 0.2358   | 0.2033          |
-| 0.1756        | 28.0  | 18424  | 0.1656          | 0.6750   | 0.2368   | 0.2002          |
-| 0.1752        | 29.0  | 19082  | 0.1650          | 0.6752   | 0.2371   | 0.2017          |
-| 0.175         | 30.0  | 19740  | 0.1647          | 0.6777   | 0.2423   | 0.2048          |
-| 0.1748        | 31.0  | 20398  | 0.1643          | 0.6796   | 0.2468   | 0.2044          |
-| 0.1742        | 32.0  | 21056  | 0.1640          | 0.6798   | 0.2473   | 0.2048          |
-| 0.1738        | 33.0  | 21714  | 0.1637          | 0.6807   | 0.2489   | 0.2037          |
-| 0.1738        | 34.0  | 22372  | 0.1632          | 0.6818   | 0.2497   | 0.2079          |
-| 0.1733        | 35.0  | 23030  | 0.1630          | 0.6831   | 0.2545   | 0.2087          |
-| 0.1726        | 36.0  | 23688  | 0.1627          | 0.6831   | 0.2538   | 0.2087          |
-| 0.1728        | 37.0  | 24346  | 0.1623          | 0.6834   | 0.2555   | 0.2130          |
-| 0.1722        | 38.0  | 25004  | 0.1621          | 0.6840   | 0.2557   | 0.2106          |
-| 0.172         | 39.0  | 25662  | 0.1617          | 0.6854   | 0.2571   | 0.2130          |
-| 0.1716        | 40.0  | 26320  | 0.1616          | 0.6857   | 0.2595   | 0.2134          |
-| 0.1715        | 41.0  | 26978  | 0.1613          | 0.6868   | 0.2627   | 0.2157          |
-| 0.1708        | 42.0  | 27636  | 0.1609          | 0.6872   | 0.2629   | 0.2153          |
-| 0.1708        | 43.0  | 28294  | 0.1608          | 0.6876   | 0.2645   | 0.2176          |
-| 0.1707        | 44.0  | 28952  | 0.1605          | 0.6879   | 0.2645   | 0.2176          |
-| 0.1706        | 45.0  | 29610  | 0.1602          | 0.6889   | 0.2656   | 0.2184          |
-| 0.1704        | 46.0  | 30268  | 0.1600          | 0.6894   | 0.2673   | 0.2192          |
-| 0.1701        | 47.0  | 30926  | 0.1599          | 0.6899   | 0.2676   | 0.2184          |
-| 0.1699        | 48.0  | 31584  | 0.1597          | 0.6907   | 0.2695   | 0.2200          |
-| 0.1696        | 49.0  | 32242  | 0.1595          | 0.6913   | 0.2694   | 0.2200          |
-| 0.1694        | 50.0  | 32900  | 0.1592          | 0.6911   | 0.2701   | 0.2223          |
-| 0.1691        | 51.0  | 33558  | 0.1591          | 0.6929   | 0.2738   | 0.2215          |
-| 0.1689        | 52.0  | 34216  | 0.1589          | 0.6924   | 0.2724   | 0.2227          |
-| 0.1689        | 53.0  | 34874  | 0.1588          | 0.6937   | 0.2761   | 0.2246          |
-| 0.1689        | 54.0  | 35532  | 0.1585          | 0.6936   | 0.2770   | 0.2243          |
-| 0.1683        | 55.0  | 36190  | 0.1583          | 0.6949   | 0.2786   | 0.2231          |
-| 0.1684        | 56.0  | 36848  | 0.1582          | 0.6946   | 0.2792   | 0.2250          |
-| 0.1677        | 57.0  | 37506  | 0.1582          | 0.6952   | 0.2832   | 0.2243          |
-| 0.168         | 58.0  | 38164  | 0.1581          | 0.6965   | 0.2855   | 0.2258          |
-| 0.1678        | 59.0  | 38822  | 0.1578          | 0.6966   | 0.2861   | 0.2277          |
-| 0.1678        | 60.0  | 39480  | 0.1576          | 0.6975   | 0.2863   | 0.2285          |
-| 0.1676        | 61.0  | 40138  | 0.1575          | 0.6975   | 0.2873   | 0.2289          |
-| 0.1676        | 62.0  | 40796  | 0.1573          | 0.6979   | 0.2880   | 0.2301          |
-| 0.1677        | 63.0  | 41454  | 0.1573          | 0.6977   | 0.2885   | 0.2285          |
-| 0.1672        | 64.0  | 42112  | 0.1571          | 0.6983   | 0.2892   | 0.2328          |
-| 0.1671        | 65.0  | 42770  | 0.1570          | 0.6990   | 0.2901   | 0.2312          |
-| 0.167         | 66.0  | 43428  | 0.1567          | 0.6984   | 0.2898   | 0.2328          |
-| 0.1671        | 67.0  | 44086  | 0.1567          | 0.6985   | 0.2892   | 0.2285          |
-| 0.1668        | 68.0  | 44744  | 0.1566          | 0.6995   | 0.2908   | 0.2305          |
-| 0.1667        | 69.0  | 45402  | 0.1564          | 0.6999   | 0.2928   | 0.2312          |
-| 0.1661        | 70.0  | 46060  | 0.1563          | 0.7003   | 0.2944   | 0.2324          |
-| 0.1665        | 71.0  | 46718  | 0.1563          | 0.7005   | 0.2932   | 0.2324          |
-| 0.1665        | 72.0  | 47376  | 0.1561          | 0.7006   | 0.2934   | 0.2340          |
-| 0.1664        | 73.0  | 48034  | 0.1560          | 0.7013   | 0.2949   | 0.2336          |
-| 0.1663        | 74.0  | 48692  | 0.1559          | 0.7012   | 0.2932   | 0.2328          |
-| 0.1662        | 75.0  | 49350  | 0.1558          | 0.7018   | 0.2960   | 0.2309          |
-| 0.1662        | 76.0  | 50008  | 0.1557          | 0.7022   | 0.2958   | 0.2316          |
-| 0.166         | 77.0  | 50666  | 0.1556          | 0.7012   | 0.2943   | 0.2340          |
-| 0.1655        | 78.0  | 51324  | 0.1555          | 0.7024   | 0.2976   | 0.2332          |
-| 0.1659        | 79.0  | 51982  | 0.1553          | 0.7020   | 0.2953   | 0.2344          |
-| 0.1657        | 80.0  | 52640  | 0.1553          | 0.7029   | 0.2972   | 0.2328          |
-| 0.1655        | 81.0  | 53298  | 0.1551          | 0.7027   | 0.2956   | 0.2347          |
-| 0.1647        | 82.0  | 53956  | 0.1551          | 0.7038   | 0.3002   | 0.2355          |
-| 0.1652        | 83.0  | 54614  | 0.1551          | 0.7036   | 0.2993   | 0.2336          |
-| 0.1649        | 84.0  | 55272  | 0.1548          | 0.7028   | 0.2977   | 0.2367          |
-| 0.1648        | 85.0  | 55930  | 0.1548          | 0.7035   | 0.2997   | 0.2367          |
-| 0.1643        | 86.0  | 56588  | 0.1548          | 0.7034   | 0.3003   | 0.2375          |
-| 0.1649        | 87.0  | 57246  | 0.1547          | 0.7041   | 0.3043   | 0.2390          |
-| 0.1647        | 88.0  | 57904  | 0.1547          | 0.7052   | 0.3051   | 0.2344          |
-| 0.1649        | 89.0  | 58562  | 0.1545          | 0.7043   | 0.3041   | 0.2390          |
-| 0.1646        | 90.0  | 59220  | 0.1544          | 0.7053   | 0.3054   | 0.2390          |
-| 0.1647        | 91.0  | 59878  | 0.1542          | 0.7055   | 0.3060   | 0.2414          |
-| 0.1646        | 92.0  | 60536  | 0.1542          | 0.7054   | 0.3062   | 0.2410          |
-| 0.1645        | 93.0  | 61194  | 0.1541          | 0.7053   | 0.3060   | 0.2425          |
-| 0.1643        | 94.0  | 61852  | 0.1541          | 0.7054   | 0.3064   | 0.2421          |
-| 0.1647        | 95.0  | 62510  | 0.1539          | 0.7069   | 0.3084   | 0.2417          |
-| 0.1645        | 96.0  | 63168  | 0.1540          | 0.7058   | 0.3068   | 0.2410          |
-| 0.1646        | 97.0  | 63826  | 0.1538          | 0.7056   | 0.3063   | 0.2414          |
-| 0.1638        | 98.0  | 64484  | 0.1539          | 0.7054   | 0.3072   | 0.2379          |
-| 0.1638        | 99.0  | 65142  | 0.1537          | 0.7060   | 0.3079   | 0.2437          |
-| 0.164         | 100.0 | 65800  | 0.1537          | 0.7077   | 0.3098   | 0.2425          |
-| 0.1636        | 101.0 | 66458  | 0.1536          | 0.7067   | 0.3087   | 0.2437          |
-| 0.1643        | 102.0 | 67116  | 0.1536          | 0.7069   | 0.3095   | 0.2425          |
-| 0.1642        | 103.0 | 67774  | 0.1535          | 0.7071   | 0.3096   | 0.2433          |
-| 0.1634        | 104.0 | 68432  | 0.1534          | 0.7074   | 0.3108   | 0.2425          |
-| 0.1639        | 105.0 | 69090  | 0.1533          | 0.7079   | 0.3111   | 0.2445          |
-| 0.1638        | 106.0 | 69748  | 0.1532          | 0.7079   | 0.3145   | 0.2437          |
-| 0.1634        | 107.0 | 70406  | 0.1532          | 0.7078   | 0.3135   | 0.2452          |
-| 0.163         | 108.0 | 71064  | 0.1531          | 0.7079   | 0.3129   | 0.2449          |
-| 0.1637        | 109.0 | 71722  | 0.1530          | 0.7086   | 0.3148   | 0.2437          |
-| 0.1636        | 110.0 | 72380  | 0.1530          | 0.7089   | 0.3149   | 0.2449          |
-| 0.1631        | 111.0 | 73038  | 0.1530          | 0.7089   | 0.3157   | 0.2441          |
-| 0.1638        | 112.0 | 73696  | 0.1530          | 0.7092   | 0.3153   | 0.2414          |
-| 0.1632        | 113.0 | 74354  | 0.1528          | 0.7087   | 0.3152   | 0.2433          |
-| 0.1631        | 114.0 | 75012  | 0.1528          | 0.7091   | 0.3143   | 0.2449          |
-| 0.1631        | 115.0 | 75670  | 0.1527          | 0.7088   | 0.3153   | 0.2437          |
-| 0.1631        | 116.0 | 76328  | 0.1526          | 0.7095   | 0.3172   | 0.2452          |
-| 0.163         | 117.0 | 76986  | 0.1526          | 0.7089   | 0.3156   | 0.2476          |
-| 0.163         | 118.0 | 77644  | 0.1524          | 0.7095   | 0.3167   | 0.2483          |
-| 0.1633        | 119.0 | 78302  | 0.1525          | 0.7101   | 0.3182   | 0.2449          |
-| 0.1628        | 120.0 | 78960  | 0.1524          | 0.7097   | 0.3181   | 0.2476          |
-| 0.163         | 121.0 | 79618  | 0.1525          | 0.7098   | 0.3172   | 0.2437          |
-| 0.163         | 122.0 | 80276  | 0.1523          | 0.7099   | 0.3184   | 0.2460          |
-| 0.1629        | 123.0 | 80934  | 0.1524          | 0.7098   | 0.3168   | 0.2425          |
-| 0.1628        | 124.0 | 81592  | 0.1523          | 0.7104   | 0.3201   | 0.2437          |
-| 0.1627        | 125.0 | 82250  | 0.1522          | 0.7104   | 0.3184   | 0.2449          |
-| 0.1627        | 126.0 | 82908  | 0.1523          | 0.7104   | 0.3203   | 0.2433          |
-| 0.1626        | 127.0 | 83566  | 0.1522          | 0.7110   | 0.3218   | 0.2441          |
-| 0.1624        | 128.0 | 84224  | 0.1521          | 0.7108   | 0.3195   | 0.2441          |
-| 0.1624        | 129.0 | 84882  | 0.1521          | 0.7105   | 0.3199   | 0.2445          |
-| 0.1627        | 130.0 | 85540  | 0.1520          | 0.7108   | 0.3192   | 0.2468          |
-| 0.1625        | 131.0 | 86198  | 0.1519          | 0.7105   | 0.3190   | 0.2460          |
-| 0.1626        | 132.0 | 86856  | 0.1520          | 0.7113   | 0.3204   | 0.2437          |
-| 0.1617        | 133.0 | 87514  | 0.1519          | 0.7109   | 0.3198   | 0.2449          |
-| 0.1624        | 134.0 | 88172  | 0.1518          | 0.7109   | 0.3199   | 0.2433          |
-| 0.1622        | 135.0 | 88830  | 0.1517          | 0.7102   | 0.3199   | 0.2464          |
-| 0.1619        | 136.0 | 89488  | 0.1517          | 0.7114   | 0.3212   | 0.2464          |
-| 0.1624        | 137.0 | 90146  | 0.1517          | 0.7121   | 0.3235   | 0.2429          |
-| 0.1622        | 138.0 | 90804  | 0.1515          | 0.7121   | 0.3220   | 0.2483          |
-| 0.1621        | 139.0 | 91462  | 0.1516          | 0.7120   | 0.3229   | 0.2456          |
-| 0.1621        | 140.0 | 92120  | 0.1516          | 0.7117   | 0.3218   | 0.2456          |
-| 0.1617        | 141.0 | 92778  | 0.1515          | 0.7116   | 0.3223   | 0.2449          |
-| 0.1622        | 142.0 | 93436  | 0.1514          | 0.7111   | 0.3230   | 0.2476          |
-| 0.162         | 143.0 | 94094  | 0.1514          | 0.7117   | 0.3214   | 0.2452          |
-| 0.162         | 144.0 | 94752  | 0.1514          | 0.7121   | 0.3232   | 0.2495          |
-| 0.1621        | 145.0 | 95410  | 0.1514          | 0.7123   | 0.3233   | 0.2476          |
-| 0.1621        | 146.0 | 96068  | 0.1513          | 0.7117   | 0.3206   | 0.2495          |
-| 0.1616        | 147.0 | 96726  | 0.1513          | 0.7125   | 0.3233   | 0.2464          |
-| 0.1619        | 148.0 | 97384  | 0.1512          | 0.7128   | 0.3241   | 0.2511          |
-| 0.1619        | 149.0 | 98042  | 0.1513          | 0.7128   | 0.3249   | 0.2487          |
-| 0.1617        | 150.0 | 98700  | 0.1513          | 0.7130   | 0.3243   | 0.2449          |
-| 0.1616        | 151.0 | 99358  | 0.1511          | 0.7122   | 0.3235   | 0.2472          |
-| 0.1616        | 152.0 | 100016 | 0.1511          | 0.7123   | 0.3246   | 0.2468          |
-| 0.1616        | 153.0 | 100674 | 0.1510          | 0.7129   | 0.3253   | 0.2503          |
-| 0.1615        | 154.0 | 101332 | 0.1510          | 0.7124   | 0.3232   | 0.2480          |
-| 0.1615        | 155.0 | 101990 | 0.1509          | 0.7125   | 0.3238   | 0.2483          |
-| 0.1618        | 156.0 | 102648 | 0.1509          | 0.7131   | 0.3251   | 0.2464          |
-| 0.1616        | 157.0 | 103306 | 0.1509          | 0.7137   | 0.3250   | 0.2495          |
-| 0.1615        | 158.0 | 103964 | 0.1508          | 0.7137   | 0.3251   | 0.2483          |
-| 0.1616        | 159.0 | 104622 | 0.1509          | 0.7138   | 0.3273   | 0.2476          |
-| 0.1617        | 160.0 | 105280 | 0.1508          | 0.7134   | 0.3238   | 0.2499          |
-| 0.1616        | 161.0 | 105938 | 0.1508          | 0.7138   | 0.3280   | 0.2491          |
-| 0.1612        | 162.0 | 106596 | 0.1508          | 0.7134   | 0.3260   | 0.2468          |
-| 0.1611        | 163.0 | 107254 | 0.1507          | 0.7136   | 0.3266   | 0.2499          |
-| 0.1614        | 164.0 | 107912 | 0.1506          | 0.7134   | 0.3258   | 0.2515          |
-| 0.1614        | 165.0 | 108570 | 0.1508          | 0.7139   | 0.3265   | 0.2483          |
-| 0.1612        | 166.0 | 109228 | 0.1506          | 0.7138   | 0.3253   | 0.2495          |
-| 0.1614        | 167.0 | 109886 | 0.1507          | 0.7144   | 0.3288   | 0.2487          |
-| 0.1615        | 168.0 | 110544 | 0.1506          | 0.7143   | 0.3294   | 0.2483          |
-| 0.1617        | 169.0 | 111202 | 0.1506          | 0.7138   | 0.3276   | 0.2464          |
-| 0.161         | 170.0 | 111860 | 0.1505          | 0.7144   | 0.3290   | 0.2487          |
-| 0.1612        | 171.0 | 112518 | 0.1505          | 0.7141   | 0.3263   | 0.2476          |
-| 0.161         | 172.0 | 113176 | 0.1504          | 0.7135   | 0.3258   | 0.2511          |
-| 0.1615        | 173.0 | 113834 | 0.1504          | 0.7142   | 0.3270   | 0.2503          |
-| 0.1609        | 174.0 | 114492 | 0.1504          | 0.7145   | 0.3278   | 0.2507          |
-| 0.1612        | 175.0 | 115150 | 0.1504          | 0.7143   | 0.3285   | 0.2499          |
-| 0.1613        | 176.0 | 115808 | 0.1503          | 0.7144   | 0.3277   | 0.2507          |
-| 0.161         | 177.0 | 116466 | 0.1504          | 0.7146   | 0.3297   | 0.2487          |
-| 0.161         | 178.0 | 117124 | 0.1504          | 0.7146   | 0.3290   | 0.2483          |
-| 0.1611        | 179.0 | 117782 | 0.1502          | 0.7146   | 0.3285   | 0.2530          |
-| 0.1609        | 180.0 | 118440 | 0.1503          | 0.7147   | 0.3301   | 0.2503          |
-| 0.1612        | 181.0 | 119098 | 0.1501          | 0.7145   | 0.3294   | 0.2507          |
-| 0.161         | 182.0 | 119756 | 0.1502          | 0.7143   | 0.3286   | 0.2503          |
-| 0.1606        | 183.0 | 120414 | 0.1501          | 0.7151   | 0.3296   | 0.2530          |
-| 0.161         | 184.0 | 121072 | 0.1501          | 0.7148   | 0.3285   | 0.2522          |
-| 0.1609        | 185.0 | 121730 | 0.1501          | 0.7148   | 0.3298   | 0.2511          |
-| 0.1609        | 186.0 | 122388 | 0.1500          | 0.7149   | 0.3285   | 0.2518          |
-| 0.1612        | 187.0 | 123046 | 0.1501          | 0.7146   | 0.3288   | 0.2483          |
-| 0.1606        | 188.0 | 123704 | 0.1500          | 0.7157   | 0.3302   | 0.2550          |
-| 0.1609        | 189.0 | 124362 | 0.1500          | 0.7156   | 0.3302   | 0.2518          |
-| 0.1605        | 190.0 | 125020 | 0.1500          | 0.7152   | 0.3295   | 0.2491          |
-| 0.161         | 191.0 | 125678 | 0.1500          | 0.7166   | 0.3332   | 0.2499          |
-| 0.1606        | 192.0 | 126336 | 0.1499          | 0.7160   | 0.3324   | 0.2495          |
-| 0.1604        | 193.0 | 126994 | 0.1500          | 0.7153   | 0.3304   | 0.2483          |
-| 0.1609        | 194.0 | 127652 | 0.1498          | 0.7161   | 0.3315   | 0.2518          |
-| 0.1609        | 195.0 | 128310 | 0.1498          | 0.7163   | 0.3304   | 0.2522          |
-| 0.1609        | 196.0 | 128968 | 0.1498          | 0.7155   | 0.3296   | 0.2518          |
-| 0.1604        | 197.0 | 129626 | 0.1498          | 0.7159   | 0.3302   | 0.2530          |
-| 0.161         | 198.0 | 130284 | 0.1498          | 0.7160   | 0.3308   | 0.2522          |
-| 0.1602        | 199.0 | 130942 | 0.1498          | 0.7161   | 0.3320   | 0.2507          |
-| 0.1606        | 200.0 | 131600 | 0.1498          | 0.7161   | 0.3314   | 0.2515          |
-| 0.1609        | 201.0 | 132258 | 0.1498          | 0.7168   | 0.3338   | 0.2483          |
-| 0.1603        | 202.0 | 132916 | 0.1497          | 0.7163   | 0.3323   | 0.2507          |
-| 0.1603        | 203.0 | 133574 | 0.1497          | 0.7169   | 0.3341   | 0.2511          |
-| 0.1603        | 204.0 | 134232 | 0.1496          | 0.7165   | 0.3338   | 0.2495          |
-| 0.1605        | 205.0 | 134890 | 0.1496          | 0.7163   | 0.3331   | 0.2518          |
-| 0.1605        | 206.0 | 135548 | 0.1495          | 0.7169   | 0.3305   | 0.2553          |
-| 0.1605        | 207.0 | 136206 | 0.1495          | 0.7166   | 0.3317   | 0.2518          |
-| 0.1601        | 208.0 | 136864 | 0.1496          | 0.7162   | 0.3318   | 0.2511          |
-| 0.1604        | 209.0 | 137522 | 0.1496          | 0.7167   | 0.3333   | 0.2526          |
-| 0.1602        | 210.0 | 138180 | 0.1494          | 0.7172   | 0.3346   | 0.2542          |
-| 0.1604        | 211.0 | 138838 | 0.1495          | 0.7170   | 0.3364   | 0.2511          |
-| 0.1608        | 212.0 | 139496 | 0.1494          | 0.7173   | 0.3355   | 0.2546          |
-| 0.16          | 213.0 | 140154 | 0.1494          | 0.7177   | 0.3370   | 0.2515          |
-| 0.1601        | 214.0 | 140812 | 0.1494          | 0.7178   | 0.3363   | 0.2546          |
-| 0.1604        | 215.0 | 141470 | 0.1493          | 0.7175   | 0.3363   | 0.2565          |
-| 0.1607        | 216.0 | 142128 | 0.1493          | 0.7168   | 0.3354   | 0.2534          |
-| 0.1598        | 217.0 | 142786 | 0.1493          | 0.7175   | 0.3354   | 0.2534          |
-| 0.1603        | 218.0 | 143444 | 0.1494          | 0.7173   | 0.3365   | 0.2518          |
-| 0.1603        | 219.0 | 144102 | 0.1493          | 0.7174   | 0.3347   | 0.2542          |
-| 0.1603        | 220.0 | 144760 | 0.1493          | 0.7174   | 0.3357   | 0.2546          |
-| 0.1602        | 221.0 | 145418 | 0.1494          | 0.7170   | 0.3373   | 0.2507          |
-| 0.1601        | 222.0 | 146076 | 0.1493          | 0.7172   | 0.3361   | 0.2542          |
-| 0.1601        | 223.0 | 146734 | 0.1492          | 0.7176   | 0.3369   | 0.2557          |
-| 0.1597        | 224.0 | 147392 | 0.1493          | 0.7177   | 0.3379   | 0.2526          |
-| 0.1602        | 225.0 | 148050 | 0.1492          | 0.7176   | 0.3377   | 0.2507          |
-| 0.16          | 226.0 | 148708 | 0.1492          | 0.7175   | 0.3359   | 0.2546          |
-| 0.1603        | 227.0 | 149366 | 0.1492          | 0.7180   | 0.3374   | 0.2522          |
-| 0.1604        | 228.0 | 150024 | 0.1492          | 0.7180   | 0.3391   | 0.2522          |
-| 0.1599        | 229.0 | 150682 | 0.1491          | 0.7177   | 0.3366   | 0.2550          |
-| 0.1596        | 230.0 | 151340 | 0.1490          | 0.7178   | 0.3368   | 0.2542          |
-| 0.1602        | 231.0 | 151998 | 0.1490          | 0.7177   | 0.3363   | 0.2553          |
-| 0.1602        | 232.0 | 152656 | 0.1491          | 0.7179   | 0.3365   | 0.2553          |
-| 0.16          | 233.0 | 153314 | 0.1491          | 0.7187   | 0.3385   | 0.2553          |
-| 0.1601        | 234.0 | 153972 | 0.1490          | 0.7176   | 0.3366   | 0.2538          |
-| 0.16          | 235.0 | 154630 | 0.1490          | 0.7175   | 0.3357   | 0.2526          |
-| 0.1603        | 236.0 | 155288 | 0.1490          | 0.7179   | 0.3375   | 0.2518          |
-| 0.1592        | 237.0 | 155946 | 0.1490          | 0.7180   | 0.3369   | 0.2534          |
-| 0.1602        | 238.0 | 156604 | 0.1490          | 0.7174   | 0.3366   | 0.2526          |
-| 0.16          | 239.0 | 157262 | 0.1490          | 0.7178   | 0.3377   | 0.2511          |
-| 0.1601        | 240.0 | 157920 | 0.1489          | 0.7180   | 0.3367   | 0.2534          |
-| 0.1602        | 241.0 | 158578 | 0.1490          | 0.7184   | 0.3381   | 0.2515          |
-| 0.16          | 242.0 | 159236 | 0.1488          | 0.7186   | 0.3375   | 0.2553          |
-| 0.1598        | 243.0 | 159894 | 0.1489          | 0.7182   | 0.3370   | 0.2553          |
-| 0.1595        | 244.0 | 160552 | 0.1489          | 0.7183   | 0.3381   | 0.2530          |
-| 0.1596        | 245.0 | 161210 | 0.1488          | 0.7175   | 0.3364   | 0.2534          |
-| 0.1599        | 246.0 | 161868 | 0.1489          | 0.7183   | 0.3374   | 0.2522          |
-| 0.1597        | 247.0 | 162526 | 0.1488          | 0.7187   | 0.3379   | 0.2542          |
-| 0.1598        | 248.0 | 163184 | 0.1488          | 0.7184   | 0.3370   | 0.2569          |
-| 0.1595        | 249.0 | 163842 | 0.1488          | 0.7181   | 0.3373   | 0.2511          |
-| 0.16          | 250.0 | 164500 | 0.1488          | 0.7183   | 0.3383   | 0.2538          |
-| 0.1598        | 251.0 | 165158 | 0.1488          | 0.7192   | 0.3387   | 0.2542          |
-| 0.1594        | 252.0 | 165816 | 0.1487          | 0.7184   | 0.3376   | 0.2561          |
-| 0.1598        | 253.0 | 166474 | 0.1487          | 0.7186   | 0.3392   | 0.2550          |
-| 0.1599        | 254.0 | 167132 | 0.1488          | 0.7183   | 0.3374   | 0.2507          |
-| 0.1595        | 255.0 | 167790 | 0.1487          | 0.7183   | 0.3377   | 0.2530          |
-| 0.1592        | 256.0 | 168448 | 0.1487          | 0.7188   | 0.3387   | 0.2515          |
-| 0.1601        | 257.0 | 169106 | 0.1487          | 0.7187   | 0.3389   | 0.2538          |
-| 0.1595        | 258.0 | 169764 | 0.1487          | 0.7187   | 0.3395   | 0.2557          |
-| 0.1592        | 259.0 | 170422 | 0.1486          | 0.7179   | 0.3373   | 0.2550          |
-| 0.1597        | 260.0 | 171080 | 0.1487          | 0.7189   | 0.3389   | 0.2526          |
-| 0.1597        | 261.0 | 171738 | 0.1487          | 0.7186   | 0.3385   | 0.2546          |
-| 0.1594        | 262.0 | 172396 | 0.1486          | 0.7186   | 0.3372   | 0.2565          |
-| 0.1598        | 263.0 | 173054 | 0.1487          | 0.7187   | 0.3380   | 0.2538          |
-| 0.1595        | 264.0 | 173712 | 0.1486          | 0.7190   | 0.3386   | 0.2553          |
-| 0.1594        | 265.0 | 174370 | 0.1485          | 0.7185   | 0.3380   | 0.2569          |
-| 0.1594        | 266.0 | 175028 | 0.1486          | 0.7190   | 0.3384   | 0.2526          |
-| 0.1594        | 267.0 | 175686 | 0.1485          | 0.7190   | 0.3385   | 0.2550          |
-| 0.1592        | 268.0 | 176344 | 0.1486          | 0.7194   | 0.3389   | 0.2557          |
-| 0.1593        | 269.0 | 177002 | 0.1486          | 0.7187   | 0.3397   | 0.2522          |
-| 0.1592        | 270.0 | 177660 | 0.1485          | 0.7195   | 0.3392   | 0.2538          |
-| 0.1597        | 271.0 | 178318 | 0.1486          | 0.7190   | 0.3389   | 0.2515          |
-| 0.1593        | 272.0 | 178976 | 0.1485          | 0.7186   | 0.3382   | 0.2557          |
-| 0.1589        | 273.0 | 179634 | 0.1485          | 0.7188   | 0.3384   | 0.2546          |
-| 0.1595        | 274.0 | 180292 | 0.1484          | 0.7190   | 0.3379   | 0.2553          |
-| 0.1593        | 275.0 | 180950 | 0.1485          | 0.7189   | 0.3382   | 0.2534          |
-| 0.1595        | 276.0 | 181608 | 0.1485          | 0.7189   | 0.3382   | 0.2542          |
-| 0.1595        | 277.0 | 182266 | 0.1485          | 0.7189   | 0.3384   | 0.2542          |
-| 0.159         | 278.0 | 182924 | 0.1484          | 0.7193   | 0.3393   | 0.2550          |
-| 0.1592        | 279.0 | 183582 | 0.1484          | 0.7191   | 0.3397   | 0.2542          |
-| 0.1592        | 280.0 | 184240 | 0.1484          | 0.7196   | 0.3395   | 0.2569          |
-| 0.1593        | 281.0 | 184898 | 0.1484          | 0.7193   | 0.3398   | 0.2565          |
-| 0.1588        | 282.0 | 185556 | 0.1484          | 0.7194   | 0.3399   | 0.2553          |
-| 0.1596        | 283.0 | 186214 | 0.1483          | 0.7195   | 0.3404   | 0.2565          |
-| 0.1591        | 284.0 | 186872 | 0.1483          | 0.7188   | 0.3383   | 0.2550          |
-| 0.1592        | 285.0 | 187530 | 0.1483          | 0.7187   | 0.3390   | 0.2534          |
-| 0.1588        | 286.0 | 188188 | 0.1484          | 0.7192   | 0.3417   | 0.2542          |
-| 0.159         | 287.0 | 188846 | 0.1483          | 0.7194   | 0.3401   | 0.2569          |
-| 0.1594        | 288.0 | 189504 | 0.1484          | 0.7199   | 0.3401   | 0.2522          |
-| 0.1589        | 289.0 | 190162 | 0.1483          | 0.7199   | 0.3407   | 0.2557          |
-| 0.1589        | 290.0 | 190820 | 0.1483          | 0.7193   | 0.3394   | 0.2546          |
-| 0.1589        | 291.0 | 191478 | 0.1482          | 0.7199   | 0.3406   | 0.2557          |
-| 0.1593        | 292.0 | 192136 | 0.1482          | 0.7192   | 0.3393   | 0.2557          |
-| 0.1595        | 293.0 | 192794 | 0.1482          | 0.7191   | 0.3398   | 0.2550          |
-| 0.1592        | 294.0 | 193452 | 0.1483          | 0.7198   | 0.3396   | 0.2565          |
-| 0.1598        | 295.0 | 194110 | 0.1482          | 0.7196   | 0.3399   | 0.2550          |
-| 0.1592        | 296.0 | 194768 | 0.1483          | 0.7202   | 0.3418   | 0.2557          |
-| 0.1596        | 297.0 | 195426 | 0.1482          | 0.7194   | 0.3395   | 0.2553          |
-| 0.1592        | 298.0 | 196084 | 0.1483          | 0.7199   | 0.3413   | 0.2526          |
-| 0.1593        | 299.0 | 196742 | 0.1482          | 0.7201   | 0.3421   | 0.2542          |
-| 0.1592        | 300.0 | 197400 | 0.1482          | 0.7204   | 0.3423   | 0.2534          |
-| 0.1591        | 301.0 | 198058 | 0.1481          | 0.7198   | 0.3420   | 0.2557          |
-| 0.159         | 302.0 | 198716 | 0.1481          | 0.7201   | 0.3411   | 0.2557          |
-| 0.1589        | 303.0 | 199374 | 0.1482          | 0.7199   | 0.3420   | 0.2550          |
-| 0.1592        | 304.0 | 200032 | 0.1481          | 0.7202   | 0.3416   | 0.2565          |
-| 0.1593        | 305.0 | 200690 | 0.1481          | 0.7202   | 0.3417   | 0.2561          |
-| 0.1588        | 306.0 | 201348 | 0.1481          | 0.7201   | 0.3425   | 0.2557          |
-| 0.159         | 307.0 | 202006 | 0.1481          | 0.7204   | 0.3417   | 0.2557          |
-| 0.1589        | 308.0 | 202664 | 0.1481          | 0.7206   | 0.3422   | 0.2561          |
-| 0.1584        | 309.0 | 203322 | 0.1481          | 0.7198   | 0.3421   | 0.2561          |
-| 0.1588        | 310.0 | 203980 | 0.1481          | 0.7211   | 0.3434   | 0.2553          |
-| 0.1591        | 311.0 | 204638 | 0.1481          | 0.7207   | 0.3435   | 0.2553          |
-| 0.159         | 312.0 | 205296 | 0.1480          | 0.7204   | 0.3429   | 0.2546          |
-| 0.1592        | 313.0 | 205954 | 0.1480          | 0.7209   | 0.3435   | 0.2550          |
-| 0.1597        | 314.0 | 206612 | 0.1480          | 0.7209   | 0.3419   | 0.2565          |
-| 0.1589        | 315.0 | 207270 | 0.1480          | 0.7202   | 0.3427   | 0.2553          |
-| 0.1588        | 316.0 | 207928 | 0.1481          | 0.7209   | 0.3440   | 0.2550          |
-| 0.1589        | 317.0 | 208586 | 0.1479          | 0.7203   | 0.3422   | 0.2565          |
-| 0.1591        | 318.0 | 209244 | 0.1479          | 0.7203   | 0.3423   | 0.2573          |
-| 0.1591        | 319.0 | 209902 | 0.1479          | 0.7201   | 0.3425   | 0.2569          |
-| 0.159         | 320.0 | 210560 | 0.1479          | 0.7206   | 0.3432   | 0.2561          |
-| 0.1588        | 321.0 | 211218 | 0.1480          | 0.7205   | 0.3433   | 0.2561          |
-| 0.1591        | 322.0 | 211876 | 0.1479          | 0.7204   | 0.3417   | 0.2573          |
-| 0.159         | 323.0 | 212534 | 0.1479          | 0.7199   | 0.3411   | 0.2565          |
-| 0.1588        | 324.0 | 213192 | 0.1479          | 0.7208   | 0.3442   | 0.2573          |
-| 0.1592        | 325.0 | 213850 | 0.1479          | 0.7204   | 0.3417   | 0.2581          |
-| 0.1588        | 326.0 | 214508 | 0.1478          | 0.7205   | 0.3418   | 0.2565          |
-| 0.1591        | 327.0 | 215166 | 0.1479          | 0.7209   | 0.3435   | 0.2581          |
-| 0.1588        | 328.0 | 215824 | 0.1479          | 0.7206   | 0.3437   | 0.2561          |
-| 0.1585        | 329.0 | 216482 | 0.1478          | 0.7206   | 0.3428   | 0.2573          |
-| 0.1587        | 330.0 | 217140 | 0.1478          | 0.7209   | 0.3429   | 0.2588          |
-| 0.1586        | 331.0 | 217798 | 0.1478          | 0.7204   | 0.3415   | 0.2573          |
-| 0.1588        | 332.0 | 218456 | 0.1478          | 0.7207   | 0.3428   | 0.2577          |
-| 0.1591        | 333.0 | 219114 | 0.1478          | 0.7212   | 0.3431   | 0.2585          |
-| 0.1592        | 334.0 | 219772 | 0.1478          | 0.7207   | 0.3435   | 0.2573          |
-| 0.1588        | 335.0 | 220430 | 0.1478          | 0.7211   | 0.3429   | 0.2592          |
-| 0.1583        | 336.0 | 221088 | 0.1477          | 0.7210   | 0.3436   | 0.2577          |
-| 0.159         | 337.0 | 221746 | 0.1478          | 0.7205   | 0.3431   | 0.2565          |
-| 0.1588        | 338.0 | 222404 | 0.1478          | 0.7206   | 0.3428   | 0.2569          |
-| 0.1587        | 339.0 | 223062 | 0.1478          | 0.7204   | 0.3419   | 0.2577          |
-| 0.1588        | 340.0 | 223720 | 0.1477          | 0.7205   | 0.3426   | 0.2577          |
-| 0.1588        | 341.0 | 224378 | 0.1477          | 0.7212   | 0.3445   | 0.2561          |
-| 0.1592        | 342.0 | 225036 | 0.1477          | 0.7206   | 0.3431   | 0.2565          |
-| 0.1587        | 343.0 | 225694 | 0.1477          | 0.7211   | 0.3429   | 0.2581          |
-| 0.1587        | 344.0 | 226352 | 0.1477          | 0.7208   | 0.3434   | 0.2577          |
-| 0.1587        | 345.0 | 227010 | 0.1477          | 0.7211   | 0.3444   | 0.2592          |
-| 0.1589        | 346.0 | 227668 | 0.1477          | 0.7206   | 0.3437   | 0.2569          |
-| 0.1588        | 347.0 | 228326 | 0.1477          | 0.7208   | 0.3447   | 0.2573          |
-| 0.1585        | 348.0 | 228984 | 0.1477          | 0.7214   | 0.3441   | 0.2577          |
-| 0.1587        | 349.0 | 229642 | 0.1477          | 0.7211   | 0.3443   | 0.2577          |
-| 0.1589        | 350.0 | 230300 | 0.1476          | 0.7214   | 0.3450   | 0.2581          |
-| 0.1592        | 351.0 | 230958 | 0.1477          | 0.7210   | 0.3445   | 0.2565          |
-| 0.1585        | 352.0 | 231616 | 0.1477          | 0.7212   | 0.3444   | 0.2581          |
-| 0.1584        | 353.0 | 232274 | 0.1477          | 0.7208   | 0.3440   | 0.2573          |
-| 0.1586        | 354.0 | 232932 | 0.1477          | 0.7217   | 0.3462   | 0.2565          |
-| 0.1589        | 355.0 | 233590 | 0.1476          | 0.7213   | 0.3439   | 0.2577          |
-| 0.1588        | 356.0 | 234248 | 0.1477          | 0.7211   | 0.3436   | 0.2581          |
-| 0.1585        | 357.0 | 234906 | 0.1476          | 0.7209   | 0.3436   | 0.2581          |
-| 0.1586        | 358.0 | 235564 | 0.1476          | 0.7216   | 0.3438   | 0.2588          |
-| 0.159         | 359.0 | 236222 | 0.1476          | 0.7210   | 0.3436   | 0.2569          |
-| 0.1587        | 360.0 | 236880 | 0.1477          | 0.7214   | 0.3446   | 0.2573          |
-| 0.1582        | 361.0 | 237538 | 0.1477          | 0.7212   | 0.3457   | 0.2581          |
-| 0.1587        | 362.0 | 238196 | 0.1476          | 0.7212   | 0.3440   | 0.2565          |
-| 0.1584        | 363.0 | 238854 | 0.1475          | 0.7215   | 0.3441   | 0.2592          |
-| 0.1587        | 364.0 | 239512 | 0.1476          | 0.7214   | 0.3445   | 0.2569          |
-| 0.1586        | 365.0 | 240170 | 0.1475          | 0.7211   | 0.3457   | 0.2577          |
-| 0.1589        | 366.0 | 240828 | 0.1475          | 0.7213   | 0.3458   | 0.2577          |
-| 0.1586        | 367.0 | 241486 | 0.1475          | 0.7215   | 0.3453   | 0.2573          |
-| 0.1588        | 368.0 | 242144 | 0.1476          | 0.7213   | 0.3448   | 0.2565          |
-| 0.1587        | 369.0 | 242802 | 0.1475          | 0.7215   | 0.3450   | 0.2577          |
-| 0.1586        | 370.0 | 243460 | 0.1475          | 0.7214   | 0.3448   | 0.2577          |
-| 0.1587        | 371.0 | 244118 | 0.1475          | 0.7215   | 0.3453   | 0.2577          |
-| 0.1585        | 372.0 | 244776 | 0.1475          | 0.7211   | 0.3443   | 0.2577          |
-| 0.1589        | 373.0 | 245434 | 0.1475          | 0.7218   | 0.3457   | 0.2577          |
-| 0.1583        | 374.0 | 246092 | 0.1475          | 0.7216   | 0.3457   | 0.2577          |
-| 0.1587        | 375.0 | 246750 | 0.1475          | 0.7216   | 0.3457   | 0.2581          |
-| 0.1587        | 376.0 | 247408 | 0.1474          | 0.7214   | 0.3449   | 0.2569          |
-| 0.1583        | 377.0 | 248066 | 0.1474          | 0.7214   | 0.3450   | 0.2573          |
-| 0.1587        | 378.0 | 248724 | 0.1475          | 0.7215   | 0.3449   | 0.2581          |
-| 0.1588        | 379.0 | 249382 | 0.1475          | 0.7211   | 0.3437   | 0.2581          |
-| 0.1585        | 380.0 | 250040 | 0.1476          | 0.7210   | 0.3442   | 0.2592          |
-| 0.1587        | 381.0 | 250698 | 0.1475          | 0.7218   | 0.3454   | 0.2592          |
-| 0.1587        | 382.0 | 251356 | 0.1475          | 0.7214   | 0.3435   | 0.2581          |
-| 0.159         | 383.0 | 252014 | 0.1474          | 0.7214   | 0.3445   | 0.2585          |
-| 0.1586        | 384.0 | 252672 | 0.1474          | 0.7216   | 0.3458   | 0.2585          |
-| 0.1584        | 385.0 | 253330 | 0.1474          | 0.7214   | 0.3447   | 0.2581          |
-| 0.1586        | 386.0 | 253988 | 0.1474          | 0.7218   | 0.3455   | 0.2585          |
-| 0.1589        | 387.0 | 254646 | 0.1474          | 0.7215   | 0.3459   | 0.2585          |
-| 0.1585        | 388.0 | 255304 | 0.1474          | 0.7219   | 0.3460   | 0.2581          |
-| 0.1582        | 389.0 | 255962 | 0.1474          | 0.7216   | 0.3457   | 0.2585          |
-| 0.1586        | 390.0 | 256620 | 0.1475          | 0.7217   | 0.3459   | 0.2569          |
-| 0.1581        | 391.0 | 257278 | 0.1473          | 0.7220   | 0.3461   | 0.2592          |
-| 0.1586        | 392.0 | 257936 | 0.1474          | 0.7220   | 0.3468   | 0.2577          |
-| 0.1585        | 393.0 | 258594 | 0.1474          | 0.7220   | 0.3462   | 0.2588          |
-| 0.1584        | 394.0 | 259252 | 0.1474          | 0.7222   | 0.3475   | 0.2577          |
-| 0.1584        | 395.0 | 259910 | 0.1474          | 0.7218   | 0.3479   | 0.2577          |
-| 0.1584        | 396.0 | 260568 | 0.1474          | 0.7219   | 0.3474   | 0.2573          |
-| 0.1582        | 397.0 | 261226 | 0.1473          | 0.7220   | 0.3460   | 0.2585          |
-| 0.1585        | 398.0 | 261884 | 0.1473          | 0.7222   | 0.3472   | 0.2588          |
-| 0.1588        | 399.0 | 262542 | 0.1473          | 0.7221   | 0.3472   | 0.2596          |
-| 0.1584        | 400.0 | 263200 | 0.1474          | 0.7223   | 0.3459   | 0.2588          |
-| 0.1578        | 401.0 | 263858 | 0.1474          | 0.7225   | 0.3472   | 0.2581          |
-| 0.1581        | 402.0 | 264516 | 0.1473          | 0.7223   | 0.3479   | 0.2573          |
-| 0.1588        | 403.0 | 265174 | 0.1474          | 0.7223   | 0.3494   | 0.2585          |
-| 0.158         | 404.0 | 265832 | 0.1474          | 0.7218   | 0.3461   | 0.2581          |
-| 0.1586        | 405.0 | 266490 | 0.1473          | 0.7217   | 0.3450   | 0.2581          |
-| 0.1582        | 406.0 | 267148 | 0.1472          | 0.7219   | 0.3462   | 0.2588          |
-| 0.1589        | 407.0 | 267806 | 0.1473          | 0.7227   | 0.3476   | 0.2588          |
-| 0.1585        | 408.0 | 268464 | 0.1473          | 0.7220   | 0.3468   | 0.2596          |
-| 0.1582        | 409.0 | 269122 | 0.1473          | 0.7221   | 0.3472   | 0.2577          |
-| 0.1582        | 410.0 | 269780 | 0.1473          | 0.7222   | 0.3471   | 0.2600          |
-| 0.1582        | 411.0 | 270438 | 0.1473          | 0.7226   | 0.3474   | 0.2592          |
-| 0.1583        | 412.0 | 271096 | 0.1473          | 0.7224   | 0.3474   | 0.2585          |
-| 0.158         | 413.0 | 271754 | 0.1472          | 0.7227   | 0.3480   | 0.2585          |
-| 0.1582        | 414.0 | 272412 | 0.1472          | 0.7219   | 0.3461   | 0.2600          |
-| 0.1583        | 415.0 | 273070 | 0.1472          | 0.7225   | 0.3468   | 0.2592          |
-| 0.1582        | 416.0 | 273728 | 0.1473          | 0.7226   | 0.3472   | 0.2592          |
-| 0.1585        | 417.0 | 274386 | 0.1472          | 0.7226   | 0.3468   | 0.2592          |
-| 0.1586        | 418.0 | 275044 | 0.1472          | 0.7226   | 0.3465   | 0.2600          |
-| 0.1583        | 419.0 | 275702 | 0.1472          | 0.7223   | 0.3463   | 0.2592          |
-| 0.1584        | 420.0 | 276360 | 0.1472          | 0.7223   | 0.3463   | 0.2588          |
-| 0.1585        | 421.0 | 277018 | 0.1472          | 0.7224   | 0.3466   | 0.2592          |
-| 0.1585        | 422.0 | 277676 | 0.1472          | 0.7223   | 0.3473   | 0.2604          |
-| 0.1585        | 423.0 | 278334 | 0.1473          | 0.7220   | 0.3473   | 0.2573          |
-| 0.1584        | 424.0 | 278992 | 0.1472          | 0.7221   | 0.3470   | 0.2592          |
-| 0.1586        | 425.0 | 279650 | 0.1471          | 0.7222   | 0.3472   | 0.2596          |
-| 0.1584        | 426.0 | 280308 | 0.1472          | 0.7229   | 0.3470   | 0.2592          |
-| 0.1579        | 427.0 | 280966 | 0.1471          | 0.7230   | 0.3485   | 0.2596          |
-| 0.1581        | 428.0 | 281624 | 0.1472          | 0.7227   | 0.3479   | 0.2585          |
-| 0.1587        | 429.0 | 282282 | 0.1472          | 0.7225   | 0.3479   | 0.2588          |
-| 0.1581        | 430.0 | 282940 | 0.1472          | 0.7230   | 0.3480   | 0.2585          |
-| 0.1582        | 431.0 | 283598 | 0.1471          | 0.7225   | 0.3476   | 0.2596          |
-| 0.158         | 432.0 | 284256 | 0.1471          | 0.7227   | 0.3479   | 0.2588          |
-| 0.1585        | 433.0 | 284914 | 0.1471          | 0.7222   | 0.3472   | 0.2592          |
-| 0.1584        | 434.0 | 285572 | 0.1471          | 0.7225   | 0.3475   | 0.2585          |
-| 0.1581        | 435.0 | 286230 | 0.1472          | 0.7232   | 0.3491   | 0.2581          |
-| 0.158         | 436.0 | 286888 | 0.1471          | 0.7227   | 0.3493   | 0.2596          |
-| 0.1586        | 437.0 | 287546 | 0.1471          | 0.7230   | 0.3490   | 0.2588          |
-| 0.1582        | 438.0 | 288204 | 0.1470          | 0.7231   | 0.3487   | 0.2596          |
-| 0.1584        | 439.0 | 288862 | 0.1471          | 0.7228   | 0.3482   | 0.2588          |
-| 0.1586        | 440.0 | 289520 | 0.1471          | 0.7229   | 0.3485   | 0.2600          |
-| 0.1583        | 441.0 | 290178 | 0.1471          | 0.7227   | 0.3482   | 0.2588          |
-| 0.1579        | 442.0 | 290836 | 0.1471          | 0.7225   | 0.3473   | 0.2592          |
-| 0.158         | 443.0 | 291494 | 0.1470          | 0.7227   | 0.3468   | 0.2585          |
-| 0.1582        | 444.0 | 292152 | 0.1471          | 0.7231   | 0.3484   | 0.2588          |
-| 0.1585        | 445.0 | 292810 | 0.1470          | 0.7225   | 0.3487   | 0.2592          |
-| 0.1583        | 446.0 | 293468 | 0.1470          | 0.7228   | 0.3483   | 0.2592          |
-| 0.1582        | 447.0 | 294126 | 0.1470          | 0.7229   | 0.3490   | 0.2588          |
-| 0.1583        | 448.0 | 294784 | 0.1470          | 0.7230   | 0.3474   | 0.2596          |
-| 0.1579        | 449.0 | 295442 | 0.1471          | 0.7228   | 0.3485   | 0.2592          |
-| 0.1584        | 450.0 | 296100 | 0.1471          | 0.7231   | 0.3487   | 0.2588          |
-| 0.1586        | 451.0 | 296758 | 0.1470          | 0.7229   | 0.3489   | 0.2600          |
-| 0.1582        | 452.0 | 297416 | 0.1470          | 0.7229   | 0.3490   | 0.2592          |
-| 0.1583        | 453.0 | 298074 | 0.1470          | 0.7228   | 0.3488   | 0.2592          |
-| 0.1578        | 454.0 | 298732 | 0.1470          | 0.7229   | 0.3486   | 0.2592          |
-| 0.1579        | 455.0 | 299390 | 0.1470          | 0.7229   | 0.3480   | 0.2588          |
-| 0.1583        | 456.0 | 300048 | 0.1470          | 0.7226   | 0.3476   | 0.2600          |
-| 0.1579        | 457.0 | 300706 | 0.1470          | 0.7228   | 0.3478   | 0.2592          |
-| 0.1579        | 458.0 | 301364 | 0.1470          | 0.7227   | 0.3474   | 0.2592          |
-| 0.1581        | 459.0 | 302022 | 0.1470          | 0.7223   | 0.3469   | 0.2600          |
-| 0.1585        | 460.0 | 302680 | 0.1470          | 0.7228   | 0.3483   | 0.2592          |
-| 0.1577        | 461.0 | 303338 | 0.1469          | 0.7230   | 0.3492   | 0.2592          |
-| 0.1584        | 462.0 | 303996 | 0.1469          | 0.7228   | 0.3482   | 0.2596          |
-| 0.158         | 463.0 | 304654 | 0.1470          | 0.7230   | 0.3487   | 0.2596          |
-| 0.158         | 464.0 | 305312 | 0.1470          | 0.7232   | 0.3490   | 0.2592          |
-| 0.1585        | 465.0 | 305970 | 0.1470          | 0.7226   | 0.3481   | 0.2585          |
-| 0.158         | 466.0 | 306628 | 0.1470          | 0.7234   | 0.3489   | 0.2588          |
-| 0.1578        | 467.0 | 307286 | 0.1470          | 0.7233   | 0.3503   | 0.2600          |
-| 0.1586        | 468.0 | 307944 | 0.1470          | 0.7232   | 0.3484   | 0.2588          |
-| 0.1581        | 469.0 | 308602 | 0.1470          | 0.7229   | 0.3479   | 0.2585          |
-| 0.1581        | 470.0 | 309260 | 0.1469          | 0.7230   | 0.3490   | 0.2608          |
-| 0.1581        | 471.0 | 309918 | 0.1470          | 0.7229   | 0.3483   | 0.2596          |
-| 0.1584        | 472.0 | 310576 | 0.1470          | 0.7232   | 0.3493   | 0.2596          |
-| 0.1586        | 473.0 | 311234 | 0.1469          | 0.7228   | 0.3476   | 0.2604          |
-| 0.1579        | 474.0 | 311892 | 0.1469          | 0.7226   | 0.3478   | 0.2608          |
-| 0.1581        | 475.0 | 312550 | 0.1469          | 0.7226   | 0.3479   | 0.2585          |
-| 0.1579        | 476.0 | 313208 | 0.1469          | 0.7226   | 0.3484   | 0.2604          |
-| 0.1583        | 477.0 | 313866 | 0.1469          | 0.7228   | 0.3477   | 0.2596          |
-| 0.1579        | 478.0 | 314524 | 0.1469          | 0.7232   | 0.3496   | 0.2596          |
-| 0.1579        | 479.0 | 315182 | 0.1468          | 0.7227   | 0.3485   | 0.2616          |
-| 0.1578        | 480.0 | 315840 | 0.1469          | 0.7228   | 0.3480   | 0.2604          |
-| 0.1578        | 481.0 | 316498 | 0.1469          | 0.7225   | 0.3483   | 0.2592          |
-| 0.1581        | 482.0 | 317156 | 0.1469          | 0.7226   | 0.3480   | 0.2600          |
-| 0.1581        | 483.0 | 317814 | 0.1469          | 0.7232   | 0.3489   | 0.2592          |
-| 0.1583        | 484.0 | 318472 | 0.1469          | 0.7231   | 0.3494   | 0.2585          |
-| 0.1582        | 485.0 | 319130 | 0.1469          | 0.7233   | 0.3497   | 0.2592          |
-| 0.1577        | 486.0 | 319788 | 0.1469          | 0.7231   | 0.3486   | 0.2596          |
-| 0.1583        | 487.0 | 320446 | 0.1469          | 0.7229   | 0.3482   | 0.2592          |
-| 0.1581        | 488.0 | 321104 | 0.1468          | 0.7230   | 0.3487   | 0.2600          |
-| 0.1578        | 489.0 | 321762 | 0.1469          | 0.7233   | 0.3494   | 0.2592          |
-| 0.1577        | 490.0 | 322420 | 0.1469          | 0.7232   | 0.3496   | 0.2585          |
-| 0.1581        | 491.0 | 323078 | 0.1469          | 0.7233   | 0.3501   | 0.2596          |
-| 0.1583        | 492.0 | 323736 | 0.1468          | 0.7228   | 0.3491   | 0.2604          |
-| 0.1581        | 493.0 | 324394 | 0.1469          | 0.7238   | 0.3503   | 0.2604          |
-| 0.1581        | 494.0 | 325052 | 0.1468          | 0.7233   | 0.3497   | 0.2616          |
-| 0.158         | 495.0 | 325710 | 0.1468          | 0.7236   | 0.3497   | 0.2604          |
-| 0.158         | 496.0 | 326368 | 0.1468          | 0.7240   | 0.3501   | 0.2604          |
-| 0.1581        | 497.0 | 327026 | 0.1468          | 0.7236   | 0.3499   | 0.2592          |
-| 0.1579        | 498.0 | 327684 | 0.1468          | 0.7235   | 0.3496   | 0.2596          |
-| 0.1578        | 499.0 | 328342 | 0.1468          | 0.7236   | 0.3496   | 0.2600          |
-| 0.158         | 500.0 | 329000 | 0.1468          | 0.7232   | 0.3494   | 0.2612          |
-| 0.1576        | 501.0 | 329658 | 0.1468          | 0.7231   | 0.3486   | 0.2600          |
-| 0.1581        | 502.0 | 330316 | 0.1468          | 0.7243   | 0.3506   | 0.2620          |
-| 0.1576        | 503.0 | 330974 | 0.1468          | 0.7234   | 0.3495   | 0.2608          |
-| 0.1584        | 504.0 | 331632 | 0.1467          | 0.7231   | 0.3487   | 0.2600          |
-| 0.158         | 505.0 | 332290 | 0.1468          | 0.7234   | 0.3485   | 0.2600          |
-| 0.1577        | 506.0 | 332948 | 0.1468          | 0.7235   | 0.3491   | 0.2592          |
-| 0.1578        | 507.0 | 333606 | 0.1467          | 0.7237   | 0.3496   | 0.2608          |
-| 0.158         | 508.0 | 334264 | 0.1468          | 0.7230   | 0.3480   | 0.2600          |
-| 0.1583        | 509.0 | 334922 | 0.1468          | 0.7238   | 0.3498   | 0.2604          |
-| 0.1583        | 510.0 | 335580 | 0.1467          | 0.7234   | 0.3492   | 0.2604          |
-| 0.1578        | 511.0 | 336238 | 0.1467          | 0.7234   | 0.3494   | 0.2588          |
-| 0.1583        | 512.0 | 336896 | 0.1468          | 0.7236   | 0.3499   | 0.2596          |
-| 0.1579        | 513.0 | 337554 | 0.1467          | 0.7233   | 0.3485   | 0.2596          |
-| 0.1579        | 514.0 | 338212 | 0.1468          | 0.7241   | 0.3500   | 0.2612          |
-| 0.1579        | 515.0 | 338870 | 0.1468          | 0.7236   | 0.3496   | 0.2612          |
-| 0.1579        | 516.0 | 339528 | 0.1467          | 0.7234   | 0.3486   | 0.2608          |
-| 0.158         | 517.0 | 340186 | 0.1468          | 0.7239   | 0.3504   | 0.2604          |
-| 0.1579        | 518.0 | 340844 | 0.1468          | 0.7241   | 0.3507   | 0.2604          |
-| 0.1577        | 519.0 | 341502 | 0.1467          | 0.7236   | 0.3496   | 0.2604          |
-| 0.1578        | 520.0 | 342160 | 0.1468          | 0.7234   | 0.3495   | 0.2604          |
-| 0.1578        | 521.0 | 342818 | 0.1468          | 0.7239   | 0.3500   | 0.2612          |
-| 0.1577        | 522.0 | 343476 | 0.1467          | 0.7241   | 0.3507   | 0.2600          |
-| 0.1579        | 523.0 | 344134 | 0.1467          | 0.7232   | 0.3495   | 0.2585          |
-| 0.1583        | 524.0 | 344792 | 0.1467          | 0.7237   | 0.3495   | 0.2608          |
-| 0.158         | 525.0 | 345450 | 0.1467          | 0.7234   | 0.3491   | 0.2604          |
-| 0.1583        | 526.0 | 346108 | 0.1467          | 0.7234   | 0.3494   | 0.2612          |
-| 0.1583        | 527.0 | 346766 | 0.1467          | 0.7235   | 0.3497   | 0.2596          |
-| 0.158         | 528.0 | 347424 | 0.1467          | 0.7236   | 0.3497   | 0.2604          |
-| 0.158         | 529.0 | 348082 | 0.1467          | 0.7235   | 0.3501   | 0.2596          |
-| 0.1579        | 530.0 | 348740 | 0.1467          | 0.7237   | 0.3500   | 0.2592          |
-| 0.1582        | 531.0 | 349398 | 0.1467          | 0.7236   | 0.3494   | 0.2608          |
-| 0.1581        | 532.0 | 350056 | 0.1468          | 0.7237   | 0.3503   | 0.2604          |
-| 0.158         | 533.0 | 350714 | 0.1467          | 0.7239   | 0.3500   | 0.2604          |
-| 0.1578        | 534.0 | 351372 | 0.1468          | 0.7238   | 0.3499   | 0.2596          |
-| 0.1581        | 535.0 | 352030 | 0.1467          | 0.7234   | 0.3491   | 0.2592          |
-| 0.158         | 536.0 | 352688 | 0.1467          | 0.7235   | 0.3495   | 0.2581          |
-| 0.1576        | 537.0 | 353346 | 0.1467          | 0.7237   | 0.3502   | 0.2592          |
-| 0.1579        | 538.0 | 354004 | 0.1466          | 0.7232   | 0.3491   | 0.2588          |
-| 0.1581        | 539.0 | 354662 | 0.1467          | 0.7235   | 0.3491   | 0.2596          |
-| 0.1578        | 540.0 | 355320 | 0.1467          | 0.7236   | 0.3499   | 0.2585          |
-| 0.1579        | 541.0 | 355978 | 0.1467          | 0.7240   | 0.3505   | 0.2588          |
-| 0.1582        | 542.0 | 356636 | 0.1467          | 0.7237   | 0.3501   | 0.2592          |
-| 0.158         | 543.0 | 357294 | 0.1467          | 0.7238   | 0.3501   | 0.2592          |
-| 0.1579        | 544.0 | 357952 | 0.1467          | 0.7243   | 0.3507   | 0.2604          |
-| 0.1582        | 545.0 | 358610 | 0.1466          | 0.7239   | 0.3500   | 0.2600          |
-| 0.1581        | 546.0 | 359268 | 0.1467          | 0.7238   | 0.3499   | 0.2596          |
-| 0.1583        | 547.0 | 359926 | 0.1466          | 0.7235   | 0.3496   | 0.2604          |
-| 0.158         | 548.0 | 360584 | 0.1466          | 0.7237   | 0.3501   | 0.2596          |
-| 0.1582        | 549.0 | 361242 | 0.1467          | 0.7239   | 0.3507   | 0.2608          |
-| 0.1577        | 550.0 | 361900 | 0.1467          | 0.7239   | 0.3503   | 0.2600          |
-| 0.1573        | 551.0 | 362558 | 0.1466          | 0.7236   | 0.3499   | 0.2604          |
-| 0.158         | 552.0 | 363216 | 0.1467          | 0.7239   | 0.3506   | 0.2608          |
-| 0.158         | 553.0 | 363874 | 0.1467          | 0.7242   | 0.3505   | 0.2612          |
-| 0.1577        | 554.0 | 364532 | 0.1466          | 0.7240   | 0.3502   | 0.2608          |
-| 0.1577        | 555.0 | 365190 | 0.1466          | 0.7237   | 0.3499   | 0.2596          |
-| 0.1573        | 556.0 | 365848 | 0.1466          | 0.7236   | 0.3503   | 0.2592          |
-| 0.1577        | 557.0 | 366506 | 0.1467          | 0.7236   | 0.3499   | 0.2588          |
-| 0.158         | 558.0 | 367164 | 0.1466          | 0.7235   | 0.3502   | 0.2585          |
-| 0.1576        | 559.0 | 367822 | 0.1466          | 0.7238   | 0.3504   | 0.2596          |
-| 0.1579        | 560.0 | 368480 | 0.1466          | 0.7235   | 0.3500   | 0.2596          |
-| 0.1574        | 561.0 | 369138 | 0.1466          | 0.7236   | 0.3497   | 0.2600          |
-| 0.1576        | 562.0 | 369796 | 0.1467          | 0.7241   | 0.3506   | 0.2608          |
-| 0.1579        | 563.0 | 370454 | 0.1466          | 0.7241   | 0.3507   | 0.2608          |
-| 0.1576        | 564.0 | 371112 | 0.1466          | 0.7237   | 0.3498   | 0.2604          |
-| 0.1579        | 565.0 | 371770 | 0.1466          | 0.7243   | 0.3507   | 0.2608          |
-| 0.1579        | 566.0 | 372428 | 0.1466          | 0.7238   | 0.3504   | 0.2604          |
-| 0.1577        | 567.0 | 373086 | 0.1467          | 0.7239   | 0.3501   | 0.2592          |
-| 0.1579        | 568.0 | 373744 | 0.1467          | 0.7243   | 0.3507   | 0.2600          |
-| 0.1577        | 569.0 | 374402 | 0.1466          | 0.7238   | 0.3502   | 0.2604          |
-| 0.1575        | 570.0 | 375060 | 0.1466          | 0.7240   | 0.3503   | 0.2612          |
-| 0.1581        | 571.0 | 375718 | 0.1466          | 0.7238   | 0.3505   | 0.2592          |
-| 0.1578        | 572.0 | 376376 | 0.1466          | 0.7238   | 0.3502   | 0.2612          |
-| 0.1583        | 573.0 | 377034 | 0.1466          | 0.7240   | 0.3501   | 0.2600          |
-| 0.1576        | 574.0 | 377692 | 0.1466          | 0.7238   | 0.3500   | 0.2592          |
-| 0.1573        | 575.0 | 378350 | 0.1466          | 0.7239   | 0.3499   | 0.2608          |
-| 0.1573        | 576.0 | 379008 | 0.1465          | 0.7238   | 0.3498   | 0.2600          |
-| 0.1573        | 577.0 | 379666 | 0.1466          | 0.7239   | 0.3500   | 0.2608          |
-| 0.158         | 578.0 | 380324 | 0.1466          | 0.7239   | 0.3502   | 0.2592          |
-| 0.1578        | 579.0 | 380982 | 0.1466          | 0.7238   | 0.3498   | 0.2604          |
-| 0.1581        | 580.0 | 381640 | 0.1466          | 0.7237   | 0.3498   | 0.2600          |
-| 0.158         | 581.0 | 382298 | 0.1466          | 0.7238   | 0.3500   | 0.2600          |
-| 0.1573        | 582.0 | 382956 | 0.1465          | 0.7238   | 0.3503   | 0.2604          |
-| 0.1586        | 583.0 | 383614 | 0.1465          | 0.7238   | 0.3502   | 0.2600          |
-| 0.1577        | 584.0 | 384272 | 0.1465          | 0.7239   | 0.3505   | 0.2596          |
-| 0.1574        | 585.0 | 384930 | 0.1466          | 0.7239   | 0.3506   | 0.2596          |
-| 0.1577        | 586.0 | 385588 | 0.1465          | 0.7238   | 0.3501   | 0.2604          |
-| 0.1574        | 587.0 | 386246 | 0.1465          | 0.7241   | 0.3505   | 0.2604          |
-| 0.1578        | 588.0 | 386904 | 0.1465          | 0.7237   | 0.3495   | 0.2596          |
-| 0.158         | 589.0 | 387562 | 0.1466          | 0.7240   | 0.3502   | 0.2600          |
-| 0.158         | 590.0 | 388220 | 0.1466          | 0.7241   | 0.3508   | 0.2604          |
-| 0.1574        | 591.0 | 388878 | 0.1466          | 0.7243   | 0.3510   | 0.2596          |
-| 0.1576        | 592.0 | 389536 | 0.1465          | 0.7234   | 0.3496   | 0.2596          |
-| 0.1582        | 593.0 | 390194 | 0.1465          | 0.7239   | 0.3504   | 0.2592          |
-| 0.158         | 594.0 | 390852 | 0.1465          | 0.7240   | 0.3505   | 0.2588          |
+The final results on the evaluation set are based on the best checkpoint at epoch 594. A truncated history of the 50 most important rows are shown below.
+For the full data, please refer to [training_log.csv](https://huggingface.co/spencercdz/xlm-roberta-sentiment-requests/blob/main/training_log.csv) in the repository.
 
+| Training Loss | Epoch | Step    | Validation Loss | F1 Micro | F1 Macro | Subset Accuracy |
+|:-------------:|:-----:|:-------:|:---------------:|:--------:|:--------:|:---------------:|
+| 0.4267        | 1.0   | 658     | 0.2727          | 0.4953   | 0.0722   | 0.1053          |
+| 0.2662        | 2.0   | 1316    | 0.2291          | 0.5446   | 0.0906   | 0.1123          |
+| 0.2366        | 3.0   | 1974    | 0.2143          | 0.5682   | 0.1031   | 0.1279          |
+| 0.2234        | 4.0   | 2632    | 0.2058          | 0.5878   | 0.1160   | 0.1333          |
+| 0.2156        | 5.0   | 3290    | 0.1997          | 0.6022   | 0.1255   | 0.1380          |
+| ...           | ...   | ...     | ...             | ...      | ...      | ...             |
+| 0.1773        | 25.0  | 16450   | 0.1670          | 0.6714   | 0.2305   | 0.1955          |
+| 0.1694        | 50.0  | 32900   | 0.1592          | 0.6911   | 0.2701   | 0.2223          |
+| 0.1662        | 75.0  | 49350   | 0.1558          | 0.7018   | 0.2960   | 0.2309          |
+| 0.164         | 100.0 | 65800   | 0.1537          | 0.7077   | 0.3098   | 0.2425          |
+| 0.1627        | 125.0 | 82250   | 0.1522          | 0.7104   | 0.3184   | 0.2449          |
+| 0.1617        | 150.0 | 98700   | 0.1513          | 0.7130   | 0.3243   | 0.2449          |
+| 0.1612        | 175.0 | 115150  | 0.1504          | 0.7143   | 0.3285   | 0.2499          |
+| 0.1606        | 200.0 | 131600  | 0.1498          | 0.7161   | 0.3314   | 0.2515          |
+| 0.16          | 250.0 | 164500  | 0.1488          | 0.7183   | 0.3383   | 0.2538          |
+| 0.1592        | 300.0 | 197400  | 0.1482          | 0.7204   | 0.3423   | 0.2534          |
+| 0.1589        | 350.0 | 230300  | 0.1476          | 0.7214   | 0.3450   | 0.2581          |
+| 0.1584        | 400.0 | 263200  | 0.1474          | 0.7223   | 0.3459   | 0.2588          |
+| 0.1584        | 450.0 | 296100  | 0.1471          | 0.7231   | 0.3487   | 0.2588          |
+| 0.158         | 500.0 | 329000  | 0.1468          | 0.7232   | 0.3494   | 0.2612          |
+| 0.1577        | 550.0 | 361900  | 0.1467          | 0.7239   | 0.3503   | 0.2600          |
+| ...           | ...   | ...     | ...             | ...      | ...      | ...             |
+| 0.1574        | 591.0 | 388878  | 0.1466          | 0.7243   | 0.3510   | 0.2596          |
+| 0.1576        | 592.0 | 389536  | 0.1465          | 0.7234   | 0.3496   | 0.2596          |
+| 0.1582        | 593.0 | 390194  | 0.1465          | 0.7239   | 0.3504   | 0.2592          |
+| 0.158         | 594.0 | 390852  | 0.1465          | 0.7240   | 0.3505   | 0.2588          |
 
 ### Framework versions