Training complete

README.md

@@ -1,68 +1,93 @@
----
-license: apache-2.0
-base_model: bert-base-cased
-tags:
-- generated_from_trainer
-metrics:
-- precision
-- recall
-- f1
-- accuracy
-model-index:
-- name: bert-finetuned-ner
-  results: []
----
-
-<!-- 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-finetuned-ner
-
-This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.0592
-- Precision: 0.9332
-- Recall: 0.9497
-- F1: 0.9414
-- Accuracy: 0.9862
-
-## 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: 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 |
-|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.0779        | 1.0   | 1756 | 0.0688          | 0.9155    | 0.9371 | 0.9261 | 0.9824   |
-| 0.0401        | 2.0   | 3512 | 0.0550          | 0.9328    | 0.9483 | 0.9405 | 0.9860   |
-| 0.0267        | 3.0   | 5268 | 0.0592          | 0.9332    | 0.9497 | 0.9414 | 0.9862   |
-
-
-### Framework versions
-
-- Transformers 4.31.0
-- Pytorch 2.0.1+cu117
-- Datasets 2.18.0
-- Tokenizers 0.13.3
+---
+library_name: transformers
+license: apache-2.0
+base_model: BSC-LT/roberta-base-bne-capitel-ner
+tags:
+- generated_from_trainer
+datasets:
+- conll2002
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: bert-finetuned-ner
+  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: 0.8599099099099099
+    - name: Recall
+      type: recall
+      value: 0.8772977941176471
+    - name: F1
+      type: f1
+      value: 0.8685168334849864
+    - name: Accuracy
+      type: accuracy
+      value: 0.978701639744725
+---
+
+<!-- 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-finetuned-ner
+
+This model is a fine-tuned version of [BSC-LT/roberta-base-bne-capitel-ner](https://huggingface.co/BSC-LT/roberta-base-bne-capitel-ner) on the conll2002 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0950
+- Precision: 0.8599
+- Recall: 0.8773
+- F1: 0.8685
+- Accuracy: 0.9787
+
+## 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: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.1045        | 1.0   | 521  | 0.0932          | 0.8593    | 0.8704 | 0.8648 | 0.9764   |
+| 0.0343        | 2.0   | 1042 | 0.0870          | 0.8616    | 0.8757 | 0.8686 | 0.9781   |
+| 0.019         | 3.0   | 1563 | 0.0950          | 0.8599    | 0.8773 | 0.8685 | 0.9787   |
+
+
+### Framework versions
+
+- Transformers 4.45.1
+- Pytorch 2.4.0
+- Datasets 2.20.0
+- Tokenizers 0.20.0