Update README.md

README.md

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----
-license: apache-2.0
-base_model: distilbert-base-uncased-finetuned-sst-2-english
-tags:
-- generated_from_trainer
-metrics:
-- accuracy
-model-index:
-- name: LLM_project
-  results: []
----
-
-<!-- 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. -->
-
-# LLM_project
-
-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 an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.0852
-- Accuracy: 0.9804
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 16
-- eval_batch_size: 16
-- seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- lr_scheduler_warmup_steps: 100
-- num_epochs: 3
-
-### Training results
-
-| Training Loss | Epoch | Step | Validation Loss | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 0.0743        | 1.0   | 1250 | 0.1208          | 0.9696   |
-| 0.145         | 2.0   | 2500 | 0.0852          | 0.9804   |
-| 0.0322        | 3.0   | 3750 | 0.1043          | 0.9822   |
-
-
-### Framework versions
-
-- Transformers 4.41.2
-- Pytorch 2.3.1+cpu
-- Datasets 2.20.0
-- Tokenizers 0.19.1
+---
+license: apache-2.0
+base_model: distilbert-base-uncased-finetuned-sst-2-english
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: LLM_project
+  results: []
+---
+
+<!-- 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. -->
+
+# LLM_project
+
+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.
+It achieves the following results on the evaluation set:
+- Loss: 0.0852
+- Accuracy: 0.9804
+
+## Model description
+
+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.
+
+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.
+
+## Intended uses & limitations
+
+**Intended Uses:**
+
+> Sentiment analysis of English text, particularly for binary classification tasks such as identifying positive and negative sentiments.
+Can be applied to product reviews, social media posts, customer feedback, etc.
+
+**Limitations:**
+
+> The model's performance is highly dependent on the quality and representativeness of the fine-tuning dataset.
+May not perform well on text data that is very different from the fine-tuning dataset.
+Limited by the scope of sentiment analysis and may not capture nuanced sentiments or complex emotions.
+Not suitable for tasks outside binary sentiment classification without further fine-tuning.
+
+## Training and evaluation data
+
+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.
+
+## Training procedure
+### Procedure
+1. Data Preprocessing: Text data was tokenized using the DistilBERT tokenizer, which converts text into a format suitable for the model.
+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.
+3. Evaluation: After training, the model was evaluated on the validation set to measure its performance in terms of loss and accuracy.
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 100
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 0.0743        | 1.0   | 1250 | 0.1208          | 0.9696   |
+| 0.145         | 2.0   | 2500 | 0.0852          | 0.9804   |
+| 0.0322        | 3.0   | 3750 | 0.1043          | 0.9822   |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.1+cpu
+- Datasets 2.20.0
+- Tokenizers 0.19.1