Training complete

README.md

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+---
+license: mit
+base_model: xlm-roberta-large
+tags:
+- generated_from_trainer
+datasets:
+- wikiann
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: xlm-roberta-large-ner-silvanus
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: wikiann
+      type: wikiann
+      config: id
+      split: validation
+      args: id
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.957203615098352
+    - name: Recall
+      type: recall
+      value: 0.9714054491502563
+    - name: F1
+      type: f1
+      value: 0.964252242602758
+    - name: Accuracy
+      type: accuracy
+      value: 0.9885975250441956
+---
+
+<!-- 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-large-ner-silvanus
+
+This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the wikiann dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0523
+- Precision: 0.9572
+- Recall: 0.9714
+- F1: 0.9643
+- Accuracy: 0.9886
+
+## 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: 5e-05
+- train_batch_size: 6
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 24
+- 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 285  | 0.0795          | 0.9222    | 0.9342 | 0.9282 | 0.9763   |
+| 0.112         | 2.0   | 570  | 0.0613          | 0.9295    | 0.9560 | 0.9426 | 0.9844   |
+| 0.112         | 3.0   | 855  | 0.0523          | 0.9572    | 0.9714 | 0.9643 | 0.9886   |
+
+
+### Framework versions
+
+- Transformers 4.35.0
+- Pytorch 2.1.0+cu118
+- Datasets 2.14.6
+- Tokenizers 0.14.1