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-v3-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-v3-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: 0.1071
+- Precision: 0.8912
+- Recall: 0.9039
+- F1: 0.8975
+- Accuracy: 0.9813
+
+## 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.1478        | 1.0   | 3615  | 0.1187          | 0.8247    | 0.8225 | 0.8236 | 0.9687   |
+| 0.0909        | 2.0   | 7230  | 0.1025          | 0.8617    | 0.8702 | 0.8659 | 0.9753   |
+| 0.0552        | 3.0   | 10845 | 0.1016          | 0.8789    | 0.8886 | 0.8837 | 0.9790   |
+| 0.0325        | 4.0   | 14460 | 0.0966          | 0.8958    | 0.8956 | 0.8957 | 0.9815   |
+| 0.0185        | 5.0   | 18075 | 0.1071          | 0.8912    | 0.9039 | 0.8975 | 0.9813   |
+
+
+### Framework versions
+
+- Transformers 4.39.3
+- Pytorch 1.12.0
+- Datasets 2.18.0
+- Tokenizers 0.15.2