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README.md

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+Entity Recognition (NER) Model Card
+Model Overview
+Model Name: NER LSTM Model
+Description: A LSTM-based model for Named Entity Recognition (NER) task. The model aims to classify words in text into their respective named entity categories such as Person, Organization, Location, etc.
+
+Intended Use
+Primary Use Case: Extracting named entities (e.g., names of people, organizations, locations) from text.
+
+Usage Instructions:
+
+Install the required libraries: Ensure that the required libraries, such as pandas, scikit-learn, keras, and tensorflow, are installed.
+Load the model and tokenizer: Use the Hugging Face Transformers library to load the model and tokenizer from the provided files.
+Tokenize input text: Preprocess input text and tokenize it using the loaded tokenizer.
+Make predictions: Feed the tokenized input through the model to obtain predictions for named entity categories.
+Post-process predictions: Use the LabelEncoder to map model predictions back to human-readable named entity categories.
+Performance and Evaluation
+Performance Metrics:
+
+Test Loss: The loss value achieved on the test dataset.
+Test Accuracy: The accuracy achieved on the test dataset.
+Training Accuracy: The accuracy achieved on the training dataset.
+Validation Accuracy: The accuracy achieved on the validation dataset.
+Performance Summary:
+
+The model achieved an accuracy of approximately [Test Accuracy] on the test dataset.
+Training and validation accuracies are provided for reference.
+Dataset
+Dataset Name: NER dataset.csv
+Description: The dataset contains labeled data for named entity recognition. It includes columns for 'Word' and 'POS' (Part-of-Speech) labels.
+
+Model Details
+Architecture:
+
+Embedding Layer: Converts input tokens into dense vectors.
+LSTM Layer: Processes the sequence of word embeddings.
+Dense Layer: Produces a probability distribution over named entity categories.
+Hyperparameters:
+
+Embedding Dimension: 100
+LSTM Units: 128
+Batch Size: 64
+Max Sequence Length: 100
+Optimizer: Adam
+Loss Function: Sparse Categorical Cross-Entropy