Model save

README.md

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+---
+base_model: UWB-AIR/Czert-B-base-cased
+tags:
+- generated_from_trainer
+datasets:
+- cnec
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: CNEC_2_0_Czert-B-base-cased
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: cnec
+      type: cnec
+      config: default
+      split: validation
+      args: default
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.8108016304347826
+    - name: Recall
+      type: recall
+      value: 0.8537195994277539
+    - name: F1
+      type: f1
+      value: 0.8317073170731707
+    - name: Accuracy
+      type: accuracy
+      value: 0.9456677151844438
+---
+
+<!-- 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. -->
+
+# CNEC_2_0_Czert-B-base-cased
+
+This model is a fine-tuned version of [UWB-AIR/Czert-B-base-cased](https://huggingface.co/UWB-AIR/Czert-B-base-cased) on the cnec dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2818
+- Precision: 0.8108
+- Recall: 0.8537
+- F1: 0.8317
+- Accuracy: 0.9457
+
+## 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: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 15
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.5129        | 2.22  | 500  | 0.2615          | 0.7602    | 0.7858 | 0.7728 | 0.9315   |
+| 0.1863        | 4.44  | 1000 | 0.2460          | 0.7845    | 0.8255 | 0.8045 | 0.9403   |
+| 0.1221        | 6.67  | 1500 | 0.2474          | 0.7969    | 0.8380 | 0.8169 | 0.9441   |
+| 0.0857        | 8.89  | 2000 | 0.2663          | 0.8028    | 0.8491 | 0.8253 | 0.9435   |
+| 0.0645        | 11.11 | 2500 | 0.2814          | 0.8081    | 0.8480 | 0.8276 | 0.9441   |
+| 0.052         | 13.33 | 3000 | 0.2818          | 0.8108    | 0.8537 | 0.8317 | 0.9457   |
+
+
+### Framework versions
+
+- Transformers 4.36.2
+- Pytorch 2.1.2+cu121
+- Datasets 2.16.1
+- Tokenizers 0.15.0