NER Training complete

README.md

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+---
+license: mit
+base_model: xlnet-large-cased
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: xlnet-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. -->
+
+# xlnet-lg-cased-ms-ner-test
+
+This model is a fine-tuned version of [xlnet-large-cased](https://huggingface.co/xlnet-large-cased) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1308
+- Precision: 0.8828
+- Recall: 0.9077
+- F1: 0.8951
+- Accuracy: 0.9814
+
+## 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.137         | 1.0   | 3615  | 0.1313          | 0.7971    | 0.7986 | 0.7979 | 0.9663   |
+| 0.0761        | 2.0   | 7230  | 0.0894          | 0.8564    | 0.8773 | 0.8667 | 0.9781   |
+| 0.0459        | 3.0   | 10845 | 0.0946          | 0.8718    | 0.8918 | 0.8817 | 0.9803   |
+| 0.021         | 4.0   | 14460 | 0.1091          | 0.8795    | 0.9017 | 0.8905 | 0.9808   |
+| 0.013         | 5.0   | 18075 | 0.1308          | 0.8828    | 0.9077 | 0.8951 | 0.9814   |
+
+
+### Framework versions
+
+- Transformers 4.39.3
+- Pytorch 1.12.0
+- Datasets 2.18.0
+- Tokenizers 0.15.2