End of training

README.md

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+---
+license: mit
+base_model: xlm-roberta-base
+tags:
+- generated_from_trainer
+datasets:
+- ncbi_disease
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: finetuned-xlm-roberta-base-NER
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: ncbi_disease
+      type: ncbi_disease
+      config: ncbi_disease
+      split: test
+      args: ncbi_disease
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.7974434611602753
+    - name: Recall
+      type: recall
+      value: 0.8447916666666667
+    - name: F1
+      type: f1
+      value: 0.8204350025290845
+    - name: Accuracy
+      type: accuracy
+      value: 0.9804874066212189
+---
+
+<!-- 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. -->
+
+# finetuned-xlm-roberta-base-NER
+
+This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the ncbi_disease dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0589
+- Precision: 0.7974
+- Recall: 0.8448
+- F1: 0.8204
+- Accuracy: 0.9805
+
+## 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: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 2
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 340  | 0.0809          | 0.6839    | 0.8698 | 0.7657 | 0.9723   |
+| 0.1092        | 2.0   | 680  | 0.0589          | 0.7974    | 0.8448 | 0.8204 | 0.9805   |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu118
+- Datasets 2.15.0
+- Tokenizers 0.15.0