Training complete

README.md

@@ -1,92 +1,66 @@
----
-license: apache-2.0
-base_model: bert-base-cased
-tags:
-- generated_from_trainer
-datasets:
-- conll2003
-metrics:
-- precision
-- recall
-- f1
-- accuracy
-model-index:
-- name: bert-finetuned-ner1
-  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: 0.9315294505857119
-    - name: Recall
-      type: recall
-      value: 0.9501851228542578
-    - name: F1
-      type: f1
-      value: 0.9407648087978006
-    - name: Accuracy
-      type: accuracy
-      value: 0.9860334373344322
----
-
-<!-- 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-ner1
-
-This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.0629
-- Precision: 0.9315
-- Recall: 0.9502
-- F1: 0.9408
-- Accuracy: 0.9860
-
-## 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 |
-|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.0763        | 1.0   | 1756 | 0.0665          | 0.8994    | 0.9339 | 0.9163 | 0.9822   |
-| 0.035         | 2.0   | 3512 | 0.0730          | 0.9302    | 0.9448 | 0.9375 | 0.9843   |
-| 0.0227        | 3.0   | 5268 | 0.0629          | 0.9315    | 0.9502 | 0.9408 | 0.9860   |
-
-
-### Framework versions
-
-- Transformers 4.41.0
-- Pytorch 2.3.0+cu121
-- Datasets 2.19.1
-- Tokenizers 0.19.1
+---
+license: apache-2.0
+base_model: bert-base-cased
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: bert-finetuned-ner1
+  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-ner1
+
+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: 0.0426
+- Precision: 0.5603
+- Recall: 0.4351
+- F1: 0.4898
+- Accuracy: 0.9892
+
+## 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: 5e-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
+- training_steps: 5000
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0508        | 0.2   | 5000 | 0.0426          | 0.5603    | 0.4351 | 0.4898 | 0.9892   |
+
+
+### Framework versions
+
+- Transformers 4.41.1
+- Pytorch 2.3.0+cpu
+- Datasets 2.19.1
+- Tokenizers 0.19.1