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
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