Add SetFit model

1_Pooling/config.json

@@ -0,0 +1,7 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false
+}

README.md

@@ -0,0 +1,288 @@
+---
+library_name: setfit
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+metrics:
+- accuracy
+widget:
+- text: 'Texas: Cop Walks Into Home She Thought Was Hers, Kills Innocent Homeowner—Not
+    Arrested'
+- text: Ellison subsequently agreed to dismiss his restraining order against her if
+    she no longer contacted him.
+- text: Gina Haspel will become the new Director of the CIA, and the first woman so
+    chosen.
+- text: At some point, the officer fired her weapon striking the victim.
+- text: Ronaldo Rauseo-Ricupero, a lawyer for the Indonesians, argued they should
+    have 90 days to move to reopen their cases after receiving copies of their administrative
+    case files and time to appeal any decision rejecting those motions.
+pipeline_tag: text-classification
+inference: false
+base_model: sentence-transformers/paraphrase-mpnet-base-v2
+model-index:
+- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.8151016456921588
+      name: Accuracy
+---
+
+# SetFit with sentence-transformers/paraphrase-mpnet-base-v2
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A OneVsRestClassifier instance is used for classification.
+
+The model has been trained using an efficient few-shot learning technique that involves:
+
+1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
+2. Training a classification head with features from the fine-tuned Sentence Transformer.
+
+## Model Details
+
+### Model Description
+- **Model Type:** SetFit
+- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
+- **Classification head:** a OneVsRestClassifier instance
+- **Maximum Sequence Length:** 512 tokens
+<!-- - **Number of Classes:** Unknown -->
+<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
+- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
+- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.8151   |
+
+## Uses
+
+### Direct Use for Inference
+
+First install the SetFit library:
+
+```bash
+pip install setfit
+```
+
+Then you can load this model and run inference.
+
+```python
+from setfit import SetFitModel
+
+# Download from the 🤗 Hub
+model = SetFitModel.from_pretrained("anismahmahi/doubt_repetition_with_noPropaganda_SetFit")
+# Run inference
+preds = model("At some point, the officer fired her weapon striking the victim.")
+```
+
+<!--
+### Downstream Use
+
+*List how someone could finetune this model on their own dataset.*
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Set Metrics
+| Training set | Min | Median  | Max |
+|:-------------|:----|:--------|:----|
+| Word count   | 1   | 20.8138 | 129 |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (2, 2)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 5
+- body_learning_rate: (2e-05, 1e-05)
+- head_learning_rate: 0.01
+- loss: CosineSimilarityLoss
+- distance_metric: cosine_distance
+- margin: 0.25
+- end_to_end: False
+- use_amp: False
+- warmup_proportion: 0.1
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: True
+
+### Training Results
+| Epoch   | Step     | Training Loss | Validation Loss |
+|:-------:|:--------:|:-------------:|:---------------:|
+| 0.0004  | 1        | 0.3567        | -               |
+| 0.0209  | 50       | 0.3286        | -               |
+| 0.0419  | 100      | 0.2663        | -               |
+| 0.0628  | 150      | 0.2378        | -               |
+| 0.0838  | 200      | 0.1935        | -               |
+| 0.1047  | 250      | 0.2549        | -               |
+| 0.1257  | 300      | 0.2654        | -               |
+| 0.1466  | 350      | 0.1668        | -               |
+| 0.1676  | 400      | 0.1811        | -               |
+| 0.1885  | 450      | 0.1884        | -               |
+| 0.2095  | 500      | 0.157         | -               |
+| 0.2304  | 550      | 0.1237        | -               |
+| 0.2514  | 600      | 0.1318        | -               |
+| 0.2723  | 650      | 0.1334        | -               |
+| 0.2933  | 700      | 0.1067        | -               |
+| 0.3142  | 750      | 0.1189        | -               |
+| 0.3351  | 800      | 0.135         | -               |
+| 0.3561  | 850      | 0.0782        | -               |
+| 0.3770  | 900      | 0.0214        | -               |
+| 0.3980  | 950      | 0.0511        | -               |
+| 0.4189  | 1000     | 0.0924        | -               |
+| 0.4399  | 1050     | 0.1418        | -               |
+| 0.4608  | 1100     | 0.0132        | -               |
+| 0.4818  | 1150     | 0.0018        | -               |
+| 0.5027  | 1200     | 0.0706        | -               |
+| 0.5237  | 1250     | 0.1502        | -               |
+| 0.5446  | 1300     | 0.133         | -               |
+| 0.5656  | 1350     | 0.0207        | -               |
+| 0.5865  | 1400     | 0.0589        | -               |
+| 0.6075  | 1450     | 0.0771        | -               |
+| 0.6284  | 1500     | 0.0241        | -               |
+| 0.6494  | 1550     | 0.0905        | -               |
+| 0.6703  | 1600     | 0.0106        | -               |
+| 0.6912  | 1650     | 0.0451        | -               |
+| 0.7122  | 1700     | 0.0011        | -               |
+| 0.7331  | 1750     | 0.0075        | -               |
+| 0.7541  | 1800     | 0.0259        | -               |
+| 0.7750  | 1850     | 0.0052        | -               |
+| 0.7960  | 1900     | 0.0464        | -               |
+| 0.8169  | 1950     | 0.0039        | -               |
+| 0.8379  | 2000     | 0.0112        | -               |
+| 0.8588  | 2050     | 0.0061        | -               |
+| 0.8798  | 2100     | 0.0143        | -               |
+| 0.9007  | 2150     | 0.0886        | -               |
+| 0.9217  | 2200     | 0.2225        | -               |
+| 0.9426  | 2250     | 0.0022        | -               |
+| 0.9636  | 2300     | 0.0035        | -               |
+| 0.9845  | 2350     | 0.002         | -               |
+| **1.0** | **2387** | **-**         | **0.2827**      |
+| 1.0054  | 2400     | 0.0315        | -               |
+| 1.0264  | 2450     | 0.0049        | -               |
+| 1.0473  | 2500     | 0.0305        | -               |
+| 1.0683  | 2550     | 0.0334        | -               |
+| 1.0892  | 2600     | 0.0493        | -               |
+| 1.1102  | 2650     | 0.0424        | -               |
+| 1.1311  | 2700     | 0.0011        | -               |
+| 1.1521  | 2750     | 0.0109        | -               |
+| 1.1730  | 2800     | 0.0009        | -               |
+| 1.1940  | 2850     | 0.0005        | -               |
+| 1.2149  | 2900     | 0.0171        | -               |
+| 1.2359  | 2950     | 0.0004        | -               |
+| 1.2568  | 3000     | 0.0717        | -               |
+| 1.2778  | 3050     | 0.0019        | -               |
+| 1.2987  | 3100     | 0.062         | -               |
+| 1.3196  | 3150     | 0.0003        | -               |
+| 1.3406  | 3200     | 0.0018        | -               |
+| 1.3615  | 3250     | 0.0011        | -               |
+| 1.3825  | 3300     | 0.0005        | -               |
+| 1.4034  | 3350     | 0.0208        | -               |
+| 1.4244  | 3400     | 0.0004        | -               |
+| 1.4453  | 3450     | 0.001         | -               |
+| 1.4663  | 3500     | 0.0003        | -               |
+| 1.4872  | 3550     | 0.0015        | -               |
+| 1.5082  | 3600     | 0.0004        | -               |
+| 1.5291  | 3650     | 0.0473        | -               |
+| 1.5501  | 3700     | 0.0092        | -               |
+| 1.5710  | 3750     | 0.032         | -               |
+| 1.5920  | 3800     | 0.0016        | -               |
+| 1.6129  | 3850     | 0.0623        | -               |
+| 1.6339  | 3900     | 0.0291        | -               |
+| 1.6548  | 3950     | 0.0386        | -               |
+| 1.6757  | 4000     | 0.002         | -               |
+| 1.6967  | 4050     | 0.0006        | -               |
+| 1.7176  | 4100     | 0.0005        | -               |
+| 1.7386  | 4150     | 0.0004        | -               |
+| 1.7595  | 4200     | 0.0004        | -               |
+| 1.7805  | 4250     | 0.0007        | -               |
+| 1.8014  | 4300     | 0.033         | -               |
+| 1.8224  | 4350     | 0.0001        | -               |
+| 1.8433  | 4400     | 0.0489        | -               |
+| 1.8643  | 4450     | 0.0754        | -               |
+| 1.8852  | 4500     | 0.0086        | -               |
+| 1.9062  | 4550     | 0.0092        | -               |
+| 1.9271  | 4600     | 0.0591        | -               |
+| 1.9481  | 4650     | 0.0013        | -               |
+| 1.9690  | 4700     | 0.0043        | -               |
+| 1.9899  | 4750     | 0.0338        | -               |
+| 2.0     | 4774     | -             | 0.3304          |
+
+* The bold row denotes the saved checkpoint.
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.1
+- Sentence Transformers: 2.2.2
+- Transformers: 4.35.2
+- PyTorch: 2.1.0+cu121
+- Datasets: 2.16.1
+- Tokenizers: 0.15.0
+
+## Citation
+
+### BibTeX
+```bibtex
+@article{https://doi.org/10.48550/arxiv.2209.11055,
+    doi = {10.48550/ARXIV.2209.11055},
+    url = {https://arxiv.org/abs/2209.11055},
+    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
+    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
+    title = {Efficient Few-Shot Learning Without Prompts},
+    publisher = {arXiv},
+    year = {2022},
+    copyright = {Creative Commons Attribution 4.0 International}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,24 @@
+{
+  "_name_or_path": "checkpoints/step_2387/",
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.35.2",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

@@ -0,0 +1,7 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.7.0",
+    "pytorch": "1.9.0+cu102"
+  }
+}

config_setfit.json

@@ -0,0 +1,4 @@
+{
+  "labels": null,
+  "normalize_embeddings": false
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b3fc287914c26c498a057b721938578d8ded6f5ccd9744f39d726ce66814509f
+size 437967672

model_head.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cf0543ded0c1d7956acd348eba536c949d166d7033262a12ee1c4bd838c8b30e
+size 20436

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,66 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "104": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "30526": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "max_length": 512,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}