Model save

README.md

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+---
+license: mit
+base_model: FacebookAI/xlm-roberta-large
+tags:
+- generated_from_trainer
+datasets:
+- cnec
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: CNEC2_0_extended_xlm-roberta-large
+  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: 0.8721153846153846
+    - name: Recall
+      type: recall
+      value: 0.9002481389578164
+    - name: F1
+      type: f1
+      value: 0.8859584859584859
+    - name: Accuracy
+      type: accuracy
+      value: 0.9766100702576113
+---
+
+<!-- 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. -->
+
+# CNEC2_0_extended_xlm-roberta-large
+
+This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the cnec dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1464
+- Precision: 0.8721
+- Recall: 0.9002
+- F1: 0.8860
+- Accuracy: 0.9766
+
+## 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: 12
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.2193        | 1.12  | 1000  | 0.1389          | 0.7636    | 0.8194 | 0.7905 | 0.9628   |
+| 0.1528        | 2.24  | 2000  | 0.1285          | 0.8106    | 0.8600 | 0.8346 | 0.9678   |
+| 0.119         | 3.36  | 3000  | 0.1278          | 0.8234    | 0.8491 | 0.8361 | 0.9679   |
+| 0.0904        | 4.48  | 4000  | 0.1104          | 0.8466    | 0.8680 | 0.8571 | 0.9747   |
+| 0.0768        | 5.6   | 5000  | 0.1269          | 0.8486    | 0.8819 | 0.8649 | 0.9734   |
+| 0.0709        | 6.72  | 6000  | 0.1293          | 0.8601    | 0.8878 | 0.8737 | 0.9759   |
+| 0.0586        | 7.84  | 7000  | 0.1404          | 0.8624    | 0.8928 | 0.8773 | 0.9753   |
+| 0.0486        | 8.96  | 8000  | 0.1445          | 0.8675    | 0.9002 | 0.8836 | 0.9766   |
+| 0.0488        | 10.08 | 9000  | 0.1467          | 0.8608    | 0.8963 | 0.8782 | 0.9753   |
+| 0.035         | 11.2  | 10000 | 0.1464          | 0.8721    | 0.9002 | 0.8860 | 0.9766   |
+
+
+### Framework versions
+
+- Transformers 4.36.2
+- Pytorch 2.1.2+cu121
+- Datasets 2.16.1
+- Tokenizers 0.15.0