Upload README.md

README.md

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+#  NER Model using Roberta
+
+
+This markdown presents a Robustly Optimized BERT Pretraining Approach (RoBERTa) model trained on a combination of two diverse datasets for two languages: English and Persian. The English dataset used is [CoNLL 2003](), while the Persian dataset is [PEYMA-ARMAN-Mixed](), a fusion of the "PEYAM" and "ARMAN" datasets, both popular for Named Entity Recognition (NER) tasks.
+
+The model training pipeline involves the following steps:
+
+Data Preparation: Cleaning, aligning, and mixing data from the two datasets.
+Data Loading: Loading the prepared data for subsequent processing.
+Tokenization: Utilizing tokenization to prepare the text data for model input.
+Token Splitting: Handling token splitting (e.g., "jack" becomes "_ja _ck") and using "-100" for optimization and ignoring certain tokens.
+Model Reconstruction: Adapting the RoBERTa model for token classification in NER tasks.
+Model Training: Training the reconstructed model on the combined dataset and evaluating its performance.
+The model's performance, as shown in the table below, demonstrates promising results:
+| Epoch | Training Loss |  Validation Loss   | F1 |  Recall  |  Precision  |  Accuracy  |
+|:-------:|:--------:|:--------:|:----------:|:--------------:|:----------:|:----------------:|
+|  1  |  0.072600 |   0.038918  |      	89.5%  |     0.906680     |     0.883703    |       0.987799    |
+|  2  |   0.027600 |  0.030184  |   92.3%   |      0.933840    |    0.915573  |        0.991334       |
+|  3  |   0.013500 |  0.030962 |    94%   |      0.946840    |    0.933740  |         0.992702        |
+|  4  |  0.006600 |   0.029897 |    94.8%   |     0.955207    |    0.941990  |         0.993574        |
+
+The model achieves an impressive F1-score of almost 95%.
+
+To use the model, the following Python code snippet can be employed:
+
+```python
+from transformers import AutoConfig, AutoTokenizer, AutoModel
+
+config = AutoConfig.from_pretrained("AliFartout/Roberta-fa-en-ner")
+tokenizer = AutoTokenizer.from_pretrained("AliFartout/Roberta-fa-en-ner")
+model = AutoModel.from_pretrained("AliFartout/Roberta-fa-en-ner")
+```
+
+By following this approach, you can seamlessly access and incorporate the trained multilingual NER model into various Natural Language Processing tasks.