update model card README.md

README.md

@@ -1,13 +1,38 @@
 ---
-license: apache-2.0
-base_model: bert-base-cased
 tags:
 - generated_from_trainer
 datasets:
-- conll2003
+- wnut_17
+metrics:
+- precision
+- recall
+- f1
+- accuracy
 model-index:
 - name: bert-finetuned-ner
-  results: []
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: wnut_17
+      type: wnut_17
+      config: wnut_17
+      split: validation
+      args: wnut_17
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.6274509803921569
+    - name: Recall
+      type: recall
+      value: 0.49760765550239233
+    - name: F1
+      type: f1
+      value: 0.5550366911274184
+    - name: Accuracy
+      type: accuracy
+      value: 0.9333784769246797
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -15,7 +40,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 # bert-finetuned-ner
 
-This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
+This model was trained from scratch on the wnut_17 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4590
+- Precision: 0.6275
+- Recall: 0.4976
+- F1: 0.5550
+- Accuracy: 0.9334
 
 ## Model description
 
@@ -42,9 +73,18 @@ The following hyperparameters were used during training:
 - lr_scheduler_type: linear
 - num_epochs: 3
 
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 425  | 0.4576          | 0.6556    | 0.4713 | 0.5484 | 0.9321   |
+| 0.0403        | 2.0   | 850  | 0.4647          | 0.6293    | 0.4629 | 0.5334 | 0.9311   |
+| 0.0227        | 3.0   | 1275 | 0.4590          | 0.6275    | 0.4976 | 0.5550 | 0.9334   |
+
+
 ### Framework versions
 
-- Transformers 4.32.1
-- Pytorch 2.0.1+cu118
-- Datasets 2.14.4
-- Tokenizers 0.13.3
+- Transformers 4.27.0.dev0
+- Pytorch 2.0.1
+- Datasets 2.12.0
+- Tokenizers 0.13.2