NER Training complete

README.md

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+---
+license: mit
+base_model: roberta-large
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: roberta-lg-cased-ms-ner-test
+  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. -->
+
+# roberta-lg-cased-ms-ner-test
+
+This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1631
+- Precision: 0.8047
+- Recall: 0.8306
+- F1: 0.8174
+- Accuracy: 0.9660
+
+## 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: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.2027        | 1.0   | 2712  | 0.1739          | 0.7335    | 0.7283 | 0.7309 | 0.9518   |
+| 0.1304        | 2.0   | 5424  | 0.1446          | 0.7860    | 0.7674 | 0.7766 | 0.9605   |
+| 0.0842        | 3.0   | 8136  | 0.1393          | 0.7892    | 0.8118 | 0.8003 | 0.9629   |
+| 0.0556        | 4.0   | 10848 | 0.1498          | 0.8001    | 0.8288 | 0.8142 | 0.9648   |
+| 0.0363        | 5.0   | 13560 | 0.1631          | 0.8047    | 0.8306 | 0.8174 | 0.9660   |
+
+
+### Framework versions
+
+- Transformers 4.39.3
+- Pytorch 1.12.0
+- Datasets 2.18.0
+- Tokenizers 0.15.2