Update README.md

README.md

@@ -1,96 +1,109 @@
----
-library_name: transformers
-license: apache-2.0
-base_model: bert-base-uncased
-tags:
-- generated_from_trainer
-datasets:
-- conll2003
-metrics:
-- precision
-- recall
-- f1
-- accuracy
-model-index:
-- name: bert-ner-conll2003
-  results:
-  - task:
-      name: Token Classification
-      type: token-classification
-    dataset:
-      name: conll2003
-      type: conll2003
-      config: conll2003
-      split: validation
-      args: conll2003
-    metrics:
-    - name: Precision
-      type: precision
-      value: 0.9414244508542268
-    - name: Recall
-      type: recall
-      value: 0.9493231905134802
-    - name: F1
-      type: f1
-      value: 0.9453573218960619
-    - name: Accuracy
-      type: accuracy
-      value: 0.9865601220074031
----
-
-<!-- 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. -->
-
-# bert-ner-conll2003
-
-This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.0631
-- Precision: 0.9414
-- Recall: 0.9493
-- F1: 0.9454
-- Accuracy: 0.9866
-
-## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: linear
-- num_epochs: 5
-- mixed_precision_training: Native AMP
-
-### Training results
-
-| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.0844        | 1.0   | 878  | 0.0693          | 0.9029    | 0.9201 | 0.9114 | 0.9806   |
-| 0.0216        | 2.0   | 1756 | 0.0559          | 0.9340    | 0.9444 | 0.9391 | 0.9854   |
-| 0.0206        | 3.0   | 2634 | 0.0569          | 0.9436    | 0.9447 | 0.9442 | 0.9863   |
-| 0.0141        | 4.0   | 3512 | 0.0634          | 0.9369    | 0.9488 | 0.9428 | 0.9860   |
-| 0.0176        | 5.0   | 4390 | 0.0631          | 0.9414    | 0.9493 | 0.9454 | 0.9866   |
-
-
-### Framework versions
-
-- Transformers 4.46.1
-- Pytorch 1.13.1+cu116
-- Datasets 3.1.0
-- Tokenizers 0.20.1
+---
+library_name: transformers
+license: apache-2.0
+base_model: bert-base-uncased
+tags:
+- generated_from_trainer
+datasets:
+- conll2003
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: bert-ner-conll2003
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: conll2003
+      type: conll2003
+      config: conll2003
+      split: validation
+      args: conll2003
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9414244508542268
+    - name: Recall
+      type: recall
+      value: 0.9493231905134802
+    - name: F1
+      type: f1
+      value: 0.9453573218960619
+    - name: Accuracy
+      type: accuracy
+      value: 0.9865601220074031
+---
+
+<!-- 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. -->
+
+# bert-ner-conll2003
+
+This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0631
+- Precision: 0.9414
+- Recall: 0.9493
+- F1: 0.9454
+- Accuracy: 0.9866
+
+## Model description
+
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import pipeline
+
+tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
+model = AutoModelForTokenClassification.from_pretrained("PassbyGrocer/bert-ner-conll2003")
+
+nlp = pipeline("ner", model=model, tokenizer=tokenizer)
+example = "My name is Wolfgang and I live in Berlin."
+
+ner_results = nlp(example)
+print(ner_results)
+
+```
+
+## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 5
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0844        | 1.0   | 878  | 0.0693          | 0.9029    | 0.9201 | 0.9114 | 0.9806   |
+| 0.0216        | 2.0   | 1756 | 0.0559          | 0.9340    | 0.9444 | 0.9391 | 0.9854   |
+| 0.0206        | 3.0   | 2634 | 0.0569          | 0.9436    | 0.9447 | 0.9442 | 0.9863   |
+| 0.0141        | 4.0   | 3512 | 0.0634          | 0.9369    | 0.9488 | 0.9428 | 0.9860   |
+| 0.0176        | 5.0   | 4390 | 0.0631          | 0.9414    | 0.9493 | 0.9454 | 0.9866   |
+
+
+### Framework versions
+
+- Transformers 4.46.1
+- Pytorch 1.13.1+cu116
+- Datasets 3.1.0
+- Tokenizers 0.20.1