End of training

README.md

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+---
+license: mit
+base_model: xlm-roberta-base
+tags:
+- generated_from_trainer
+datasets:
+- wikiann
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: xlm-roberta-base-finetuned-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: wikiann
+      type: wikiann
+      config: ka
+      split: validation
+      args: ka
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.8505682876839947
+    - name: Recall
+      type: recall
+      value: 0.8702816057519472
+    - name: F1
+      type: f1
+      value: 0.8603120330609663
+    - name: Accuracy
+      type: accuracy
+      value: 0.9424682155180856
+---
+
+<!-- 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. -->
+
+# xlm-roberta-base-finetuned-ner
+
+This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the wikiann dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2031
+- Precision: 0.8506
+- Recall: 0.8703
+- F1: 0.8603
+- Accuracy: 0.9425
+
+## 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: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.5349        | 1.0   | 625  | 0.2377          | 0.8302    | 0.8218 | 0.8260 | 0.9287   |
+| 0.2353        | 2.0   | 1250 | 0.2037          | 0.8556    | 0.8536 | 0.8546 | 0.9394   |
+| 0.1782        | 3.0   | 1875 | 0.2031          | 0.8506    | 0.8703 | 0.8603 | 0.9425   |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu118
+- Datasets 2.15.0
+- Tokenizers 0.15.0

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