update model card README.md

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- wikiann
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: roberta-large_ner_wikiann
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: wikiann
+      type: wikiann
+      args: en
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.8462551098177787
+    - name: Recall
+      type: recall
+      value: 0.8634242895518167
+    - name: F1
+      type: f1
+      value: 0.8547534903250638
+    - name: Accuracy
+      type: accuracy
+      value: 0.9382388000397338
+---
+
+<!-- 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-large_ner_wikiann
+
+This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the wikiann dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2783
+- Precision: 0.8463
+- Recall: 0.8634
+- F1: 0.8548
+- Accuracy: 0.9382
+
+## 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: cosine
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.3395        | 1.0   | 1250 | 0.2652          | 0.8039    | 0.8308 | 0.8171 | 0.9242   |
+| 0.2343        | 2.0   | 2500 | 0.2431          | 0.8354    | 0.8503 | 0.8428 | 0.9329   |
+| 0.1721        | 3.0   | 3750 | 0.2315          | 0.8330    | 0.8503 | 0.8416 | 0.9352   |
+| 0.1156        | 4.0   | 5000 | 0.2709          | 0.8477    | 0.8634 | 0.8554 | 0.9385   |
+| 0.1026        | 5.0   | 6250 | 0.2783          | 0.8463    | 0.8634 | 0.8548 | 0.9382   |
+
+
+### Framework versions
+
+- Transformers 4.20.1
+- Pytorch 1.11.0
+- Datasets 2.1.0
+- Tokenizers 0.12.1