update model card README.md

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- conll2003
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: test-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: conll2003
+      type: conll2003
+      args: conll2003
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9467731204258151
+    - name: Recall
+      type: recall
+      value: 0.9579266240323123
+    - name: F1
+      type: f1
+      value: 0.952317215994646
+    - name: Accuracy
+      type: accuracy
+      value: 0.9920953233908337
+---
+
+<!-- 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. -->
+
+# test-ner
+
+This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the conll2003 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0398
+- Precision: 0.9468
+- Recall: 0.9579
+- F1: 0.9523
+- Accuracy: 0.9921
+
+## 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: 1
+- eval_batch_size: 2
+- seed: 42
+- distributed_type: IPU
+- total_train_batch_size: 16
+- total_eval_batch_size: 10
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3.0
+- training precision: Mixed Precision
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.20.0
+- Pytorch 1.10.0+cpu
+- Datasets 2.4.0
+- Tokenizers 0.12.1