Training complete

README.md

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+---
+license: apache-2.0
+base_model: distilbert-base-cased
+tags:
+- generated_from_trainer
+datasets:
+- bionlp2004
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: distilbert-base-cased-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: bionlp2004
+      type: bionlp2004
+      config: bionlp2004
+      split: validation
+      args: bionlp2004
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.7436025257560651
+    - name: Recall
+      type: recall
+      value: 0.8058707005222402
+    - name: F1
+      type: f1
+      value: 0.7734854377322617
+    - name: Accuracy
+      type: accuracy
+      value: 0.9356447830424587
+---
+
+<!-- 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. -->
+
+# distilbert-base-cased-ner
+
+This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the bionlp2004 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2048
+- Precision: 0.7436
+- Recall: 0.8059
+- F1: 0.7735
+- Accuracy: 0.9356
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.2313        | 1.0   | 2078 | 0.2127          | 0.7120    | 0.7810 | 0.7449 | 0.9287   |
+| 0.184         | 2.0   | 4156 | 0.1992          | 0.7258    | 0.7999 | 0.7611 | 0.9353   |
+| 0.1471        | 3.0   | 6234 | 0.2048          | 0.7436    | 0.8059 | 0.7735 | 0.9356   |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.16.1
+- Tokenizers 0.15.1