update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- wikiann
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: distilbert-base-uncased_ner_wikiann
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: wikiann
+      type: wikiann
+      args: en
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.8138623392697518
+    - name: Recall
+      type: recall
+      value: 0.8367029548989113
+    - name: F1
+      type: f1
+      value: 0.8251246122207119
+    - name: Accuracy
+      type: accuracy
+      value: 0.9300437071620145
+---
+
+<!-- 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. -->
+
+# distilbert-base-uncased_ner_wikiann
+
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wikiann dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2834
+- Precision: 0.8139
+- Recall: 0.8367
+- F1: 0.8251
+- Accuracy: 0.9300
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.3325        | 1.0   | 1250 | 0.2657          | 0.7732    | 0.8175 | 0.7947 | 0.9214   |
+| 0.2242        | 2.0   | 2500 | 0.2505          | 0.7942    | 0.8289 | 0.8111 | 0.9262   |
+| 0.158         | 3.0   | 3750 | 0.2539          | 0.8099    | 0.8367 | 0.8231 | 0.9294   |
+| 0.1155        | 4.0   | 5000 | 0.2804          | 0.8172    | 0.8373 | 0.8271 | 0.9302   |
+| 0.1047        | 5.0   | 6250 | 0.2834          | 0.8139    | 0.8367 | 0.8251 | 0.9300   |
+
+
+### Framework versions
+
+- Transformers 4.20.1
+- Pytorch 1.11.0
+- Datasets 2.1.0
+- Tokenizers 0.12.1