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--- |
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license: apache-2.0 |
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base_model: distilbert-base-uncased-finetuned-sst-2-english |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: LLM_project |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# LLM_project |
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This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on IMDb reviews dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0852 |
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- Accuracy: 0.9804 |
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## Model description |
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This model is a fine-tuned version of the DistilBERT model, which is a smaller, faster, and lighter version of BERT (Bidirectional Encoder Representations from Transformers). The base model has been pre-trained on a large corpus of English data in a self-supervised fashion, and fine-tuning was performed using a sentiment analysis dataset. The model is uncased, meaning it does not distinguish between uppercase and lowercase letters. |
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DistilBERT retains 97% of BERT's language understanding while being 60% faster and 40% smaller, making it highly efficient for various NLP tasks including sentiment analysis, which this model is specifically tuned for. |
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## Intended uses & limitations |
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**Intended Uses:** |
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> Sentiment analysis of English text, particularly for binary classification tasks such as identifying positive and negative sentiments. |
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Can be applied to product reviews, social media posts, customer feedback, etc. |
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**Limitations:** |
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> The model's performance is highly dependent on the quality and representativeness of the fine-tuning dataset. |
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May not perform well on text data that is very different from the fine-tuning dataset. |
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Limited by the scope of sentiment analysis and may not capture nuanced sentiments or complex emotions. |
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Not suitable for tasks outside binary sentiment classification without further fine-tuning. |
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## Training and evaluation data |
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The model was evaluated on a separate validation set that was not seen during training. This evaluation set is also designed for sentiment analysis and includes examples that reflect real-world use cases. |
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## Training procedure |
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### Procedure |
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1. Data Preprocessing: Text data was tokenized using the DistilBERT tokenizer, which converts text into a format suitable for the model. |
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2. Model Fine-Tuning: The pre-trained DistilBERT model was fine-tuned on the training dataset. Fine-tuning involves adjusting the weights of the model to better fit the sentiment analysis task. |
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3. Evaluation: After training, the model was evaluated on the validation set to measure its performance in terms of loss and accuracy. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.0743 | 1.0 | 1250 | 0.1208 | 0.9696 | |
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| 0.145 | 2.0 | 2500 | 0.0852 | 0.9804 | |
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| 0.0322 | 3.0 | 3750 | 0.1043 | 0.9822 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.1+cpu |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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