update model card README.md

README.md

@@ -0,0 +1,103 @@
+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- lextreme
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: distilroberta-base-mapa_coarse-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: lextreme
+      type: lextreme
+      config: mapa_coarse
+      split: test
+      args: mapa_coarse
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.7440758293838863
+    - name: Recall
+      type: recall
+      value: 0.5805042016806723
+    - name: F1
+      type: f1
+      value: 0.652190332326284
+    - name: Accuracy
+      type: accuracy
+      value: 0.9871584939520047
+---
+
+<!-- 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. -->
+
+# distilroberta-base-mapa_coarse-ner
+
+This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the lextreme dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1020
+- Precision: 0.7441
+- Recall: 0.5805
+- F1: 0.6522
+- Accuracy: 0.9872
+
+## 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: linear
+- num_epochs: 15
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0343        | 1.0   | 1739  | 0.0694          | 0.6342    | 0.5205 | 0.5718 | 0.9841   |
+| 0.0263        | 2.0   | 3478  | 0.0705          | 0.7961    | 0.5235 | 0.6317 | 0.9865   |
+| 0.0183        | 3.0   | 5217  | 0.0670          | 0.7417    | 0.5313 | 0.6191 | 0.9864   |
+| 0.015         | 4.0   | 6956  | 0.0632          | 0.7237    | 0.5850 | 0.6470 | 0.9869   |
+| 0.0137        | 5.0   | 8695  | 0.0663          | 0.7311    | 0.6064 | 0.6629 | 0.9872   |
+| 0.011         | 6.0   | 10434 | 0.0703          | 0.7163    | 0.5877 | 0.6457 | 0.9868   |
+| 0.0096        | 7.0   | 12173 | 0.0799          | 0.7511    | 0.5676 | 0.6466 | 0.9871   |
+| 0.0071        | 8.0   | 13912 | 0.0770          | 0.7386    | 0.5640 | 0.6396 | 0.9868   |
+| 0.0068        | 9.0   | 15651 | 0.0827          | 0.7285    | 0.5674 | 0.6379 | 0.9868   |
+| 0.0057        | 10.0  | 17390 | 0.0897          | 0.7611    | 0.5719 | 0.6531 | 0.9872   |
+| 0.0053        | 11.0  | 19129 | 0.0940          | 0.7614    | 0.5627 | 0.6471 | 0.9871   |
+| 0.004         | 12.0  | 20868 | 0.0874          | 0.7184    | 0.6084 | 0.6588 | 0.9873   |
+| 0.0035        | 13.0  | 22607 | 0.0986          | 0.7513    | 0.5766 | 0.6525 | 0.9872   |
+| 0.003         | 14.0  | 24346 | 0.1012          | 0.7396    | 0.5805 | 0.6505 | 0.9871   |
+| 0.0026        | 15.0  | 26085 | 0.1020          | 0.7441    | 0.5805 | 0.6522 | 0.9872   |
+
+
+### Framework versions
+
+- Transformers 4.26.0
+- Pytorch 1.13.1+cu117
+- Datasets 2.9.0
+- Tokenizers 0.13.2