Model files uploaded

.gitattributes

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README.md

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 ---
-license: wtfpl
+license: bsd-3-clause
+  
+language:
+- en
+  
+tags:
+- sentiment
+- bert
+- sentiment-analysis
+- transformers
+
+pipeline_tag: text-classification
 ---
+> Authors : GRP209
+
+# User Comment Sentiment Analysis
+
+This model aims to analyze user comments on products and extracting the expressed sentiments. 
+
+User ratings on the internet do not always provide detailed qualitative information about their experience. 
+
+Therefore, it is important to go beyond these ratings and extract more insightful information that can help a brand improve their product or service.
+
+# Objective
+
+The model utilizes the BERT architecture and is trained on a dataset of user comments with sentiment labels.
+
+The model is capable of analyzing comments and extracting sentiments such as **positive**, **negative**, or **neutral**.
+
+# Features
+
+**Sentiment Classification**: The model can classify user comments into positive, negative, or neutral sentiments, providing an overall indication of the expressed opinion.
+
+**Improvement Suggestions**: In cases where a comment expresses a negative or neutral sentiment, the model suggests an improved version of the text with a more positive sentiment. 
+This can help businesses understand consumer reactions and identify areas for product or service improvement.
+
+# Usage
+
+To use this sentiment analysis system, follow these steps:
+
+- Install the required dependencies by running the command pip install -r requirements.txt.
+- Once the training is complete, the best-trained model will be saved in the best_model_state.bin file.
+- To make predictions on new comments, use the analyze_sentiment(comment_text) function, replacing comment_text with the actual comment text to analyze. 
+- The model will return the sentiment expressed in the comment.
+- To suggest an improved version of a comment, use the suggest_improved_text(comment_text) function. 
+- If the comment expresses a negative or neutral sentiment, the function will generate an improved version of the text with a more positive sentiment. Otherwise, the original text will be returned without modification.

config.json

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+{
+  "_name_or_path": "AutoTrain",
+  "_num_labels": 3,
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "negative",
+    "1": "neutral",
+    "2": "positive"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "negative": 0,
+    "neutral": 1,
+    "positive": 2
+  },
+  "layer_norm_eps": 1e-12,
+  "max_length": 64,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "padding": "max_length",
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.30.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.pkl

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pytorch_model.bin

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special_tokens_map.json

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+{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}

tokenizer_config.json

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