update model card README.md

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- conllpp
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: roberta-large-md-conllpp
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: conllpp
+      type: conllpp
+      config: conllpp
+      split: train
+      args: conllpp
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9971177780689113
+    - name: Recall
+      type: recall
+      value: 0.9968043586452576
+    - name: F1
+      type: f1
+      value: 0.9969610437242934
+    - name: Accuracy
+      type: accuracy
+      value: 0.995003768708948
+---
+
+<!-- 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-md-conllpp
+
+This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the conllpp dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0457
+- Precision: 0.9971
+- Recall: 0.9968
+- F1: 0.9970
+- Accuracy: 0.9950
+
+## 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: 1e-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: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0748        | 1.0   | 878  | 0.0309          | 0.9959    | 0.9962 | 0.9961 | 0.9935   |
+| 0.0111        | 2.0   | 1756 | 0.0346          | 0.9974    | 0.9967 | 0.9970 | 0.9951   |
+| 0.0057        | 3.0   | 2634 | 0.0348          | 0.9974    | 0.9960 | 0.9967 | 0.9946   |
+| 0.0031        | 4.0   | 3512 | 0.0434          | 0.9976    | 0.9964 | 0.9970 | 0.9951   |
+| 0.0017        | 5.0   | 4390 | 0.0457          | 0.9971    | 0.9968 | 0.9970 | 0.9950   |
+
+
+### Framework versions
+
+- Transformers 4.21.2
+- Pytorch 1.12.1
+- Datasets 2.4.0
+- Tokenizers 0.12.1