End of training

README.md

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+---
+license: apache-2.0
+base_model: distilbert-base-uncased
+tags:
+- generated_from_trainer
+datasets:
+- szeged_ner
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: test-train-model
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: szeged_ner
+      type: szeged_ner
+      config: business
+      split: validation
+      args: business
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9325044404973357
+    - name: Recall
+      type: recall
+      value: 0.9308510638297872
+    - name: F1
+      type: f1
+      value: 0.9316770186335402
+    - name: Accuracy
+      type: accuracy
+      value: 0.9925327242378986
+---
+
+<!-- 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. -->
+
+# test-train-model
+
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the szeged_ner dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0319
+- Precision: 0.9325
+- Recall: 0.9309
+- F1: 0.9317
+- Accuracy: 0.9925
+
+## 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: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.2029        | 1.0   | 511  | 0.0493          | 0.8734    | 0.8564 | 0.8648 | 0.9873   |
+| 0.0756        | 2.0   | 1022 | 0.0381          | 0.8930    | 0.9025 | 0.8977 | 0.9897   |
+| 0.0489        | 3.0   | 1533 | 0.0327          | 0.925     | 0.9184 | 0.9217 | 0.9921   |
+| 0.0339        | 4.0   | 2044 | 0.0323          | 0.9385    | 0.9202 | 0.9293 | 0.9926   |
+| 0.0258        | 5.0   | 2555 | 0.0319          | 0.9325    | 0.9309 | 0.9317 | 0.9925   |
+
+
+### Framework versions
+
+- Transformers 4.32.0
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.4
+- Tokenizers 0.13.3