Training complete with metrics

README.md

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+---
+license: apache-2.0
+base_model: bert-base-cased
+tags:
+- generated_from_trainer
+datasets:
+- conll2002
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: bert-base-cased-finetuned-conll2002
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: conll2002
+      type: conll2002
+      config: es
+      split: validation
+      args: es
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.7785542694911552
+    - name: Recall
+      type: recall
+      value: 0.8191636029411765
+    - name: F1
+      type: f1
+      value: 0.7983428507445974
+    - name: Accuracy
+      type: accuracy
+      value: 0.9700584273152552
+---
 
-# BERT Base Cased finetuned on CONLL2002
+<!-- 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-base-cased-finetuned-conll2002
+
+This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2002 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2269
+- Precision: 0.7786
+- Recall: 0.8192
+- F1: 0.7983
+- Accuracy: 0.9701
 
 ## Model description
-This is a BERT base cased model fine-tuned on the CONLL2002 dataset for Named Entity Recognition (NER).
 
-## Metrics
-- Precision: 0.7785542694911552
-- Recall: 0.8191636029411765
-- F1-score: 0.7983428507445974
-- Accuracy: 0.9700584273152552
+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: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0146        | 1.0   | 1041 | 0.2217          | 0.7523    | 0.8104 | 0.7803 | 0.9668   |
+| 0.0082        | 2.0   | 2082 | 0.2148          | 0.7758    | 0.8143 | 0.7946 | 0.9696   |
+| 0.0064        | 3.0   | 3123 | 0.2269          | 0.7786    | 0.8192 | 0.7983 | 0.9701   |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.2
+- Tokenizers 0.19.1

runs/Jun06_13-12-08_861bea3226f3/events.out.tfevents.1717681687.861bea3226f3.683.7

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