Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- ruanchaves/faquad-nli
+language:
+- pt
+metrics:
+- accuracy
+library_name: transformers
+pipeline_tag: text-classification
+tags:
+- textual-entailment
+widget:
+- text: "<s>Qual a capital do Brasil?<s>A capital do Brasil é Brasília!</s>"
+  example_title: Exemplo
+- text: "<s>Qual a capital do Brasil?<s>Anões são muito mais legais do que elfos!</s>"
+  example_title: Exemplo
+---
+# TeenyTinyLlama-160m-FaQuAD-NLI
+
+TeenyTinyLlama is a series of small foundational models trained in Brazilian Portuguese.
+
+This repository contains a version of [TeenyTinyLlama-460m](https://huggingface.co/nicholasKluge/TeenyTinyLlama-160m) (`TeenyTinyLlama-160m-FaQuAD-NLI`) fine-tuned on the [FaQuAD-NLI dataset](https://huggingface.co/datasets/ruanchaves/faquad-nli).
+
+## Details
+
+- **Number of Epochs:** 3
+- **Batch size:** 16
+- **Optimizer:** `torch.optim.AdamW` (learning_rate = 4e-5, epsilon = 1e-8)
+- **GPU:** 1 NVIDIA A100-SXM4-40GB
+
+## Usage
+
+Using `transformers.pipeline`:
+
+```python
+from transformers import pipeline
+
+text = "<s>Qual a capital do Brasil?<s>A capital do Brasil é Brasília!</s>"
+
+classifier = pipeline("text-classification", model="nicholasKluge/TeenyTinyLlama-460m-FaQuAD-NLI")
+classifier(text)
+
+# >>> [{'label': 'SUITABLE', 'score': 0.9774010181427002}]
+```
+
+## Reproducing
+
+To reproduce the fine-tuning process, use the following code snippet:
+
+```python
+# Faquad-nli
+! pip install transformers datasets evaluate accelerate -q
+
+import evaluate
+import numpy as np
+from datasets import load_dataset, Dataset, DatasetDict
+from transformers import AutoTokenizer, DataCollatorWithPadding
+from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer
+
+# Load the task
+dataset = load_dataset("ruanchaves/faquad-nli")
+
+# Create a `ModelForSequenceClassification`
+model = AutoModelForSequenceClassification.from_pretrained(
+    "nicholasKluge/TeenyTinyLlama-460m", 
+    num_labels=2, 
+    id2label={0: "UNSUITABLE", 1: "SUITABLE"}, 
+    label2id={"UNSUITABLE": 0, "SUITABLE": 1}
+)
+
+tokenizer = AutoTokenizer.from_pretrained("nicholasKluge/TeenyTinyLlama-460m")
+
+# Format the dataset
+train = dataset['train'].to_pandas()
+train['text'] = train['question'] + tokenizer.bos_token + train['answer'] + tokenizer.eos_token
+train = train[['text', 'label']]
+train.labels = train.label.astype(int)
+train = Dataset.from_pandas(train)
+
+test = dataset['test'].to_pandas()
+test['text'] = test['question'] + tokenizer.bos_token + test['answer'] + tokenizer.eos_token
+test = test[['text', 'label']]
+test.labels = test.label.astype(int)
+test = Dataset.from_pandas(test)
+
+dataset = DatasetDict({
+    "train": train,  
+    "test": test                  
+})
+
+# Preprocess the dataset
+def preprocess_function(examples):
+    return tokenizer(examples["text"], truncation=True)
+
+dataset_tokenized = dataset.map(preprocess_function, batched=True)
+
+# Create a simple data collactor
+data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
+
+# Use accuracy as evaluation metric
+accuracy = evaluate.load("accuracy")
+
+# Function to compute accuracy
+def compute_metrics(eval_pred):
+    predictions, labels = eval_pred
+    predictions = np.argmax(predictions, axis=1)
+    return accuracy.compute(predictions=predictions, references=labels)
+
+# Define training arguments
+training_args = TrainingArguments(
+    output_dir="checkpoints",
+    learning_rate=4e-5,
+    per_device_train_batch_size=16,
+    per_device_eval_batch_size=16,
+    num_train_epochs=3,
+    weight_decay=0.01,
+    evaluation_strategy="epoch",
+    save_strategy="epoch",
+    load_best_model_at_end=True,
+    push_to_hub=True,
+    hub_token="your_token_here",
+    hub_model_id="username/model-ID"
+)
+
+# Define the Trainer
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=dataset_tokenized["train"],
+    eval_dataset=dataset_tokenized["test"],
+    tokenizer=tokenizer,
+    data_collator=data_collator,
+    compute_metrics=compute_metrics,
+)
+
+# Train!
+trainer.train()
+
+```
+
+## Fine-Tuning Comparisons
+
+| Models                                                                                     | [FaQuAD-NLI](https://huggingface.co/datasets/ruanchaves/faquad-nli) |
+|--------------------------------------------------------------------------------------------|---------------------------------------------------------------------|
+| [Bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) | 93.07                                                               |
+| [Bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased)| 92.26                                                               |
+| [Teeny Tiny Llama 460m](https://huggingface.co/nicholasKluge/TeenyTinyLlama-460m)          | 91.18                                                               |
+| [Teeny Tiny Llama 160m](https://huggingface.co/nicholasKluge/TeenyTinyLlama-160m)          | 90.00                                                               |
+| [Gpt2-small-portuguese](https://huggingface.co/pierreguillou/gpt2-small-portuguese)        | 86.46                                                               |
+
+## Cite as 🤗
+
+```latex
+
+@misc{nicholas22llama,
+  doi = {10.5281/zenodo.6989727},
+  url = {https://huggingface.co/nicholasKluge/TeenyTinyLlama-460m},
+  author = {Nicholas Kluge Corrêa},
+  title = {TeenyTinyLlama},
+  year = {2023},
+  publisher = {HuggingFace},
+  journal = {HuggingFace repository},
+}
+
+```
+
+## Funding
+
+This repository was built as part of the RAIES ([Rede de Inteligência Artificial Ética e Segura](https://www.raies.org/)) initiative, a project supported by FAPERGS - ([Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul](https://fapergs.rs.gov.br/inicial)), Brazil.
+
+## License
+
+TeenyTinyLlama-460m-FaQuAD-NLI is licensed under the Apache License, Version 2.0. See the [LICENSE](LICENSE) file for more details.