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_Supertypes_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: 0.8317152103559871
+    - name: Recall
+      type: recall
+      value: 0.8682432432432432
+    - name: F1
+      type: f1
+      value: 0.8495867768595041
+    - name: Accuracy
+      type: accuracy
+      value: 0.9680139069969579
+---
+
+<!-- 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_Supertypes_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: 0.2072
+- Precision: 0.8317
+- Recall: 0.8682
+- F1: 0.8496
+- Accuracy: 0.9680
+
+## 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: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.2727        | 1.11  | 500  | 0.1414          | 0.7268    | 0.8012 | 0.7622 | 0.9594   |
+| 0.1146        | 2.22  | 1000 | 0.1338          | 0.7697    | 0.8581 | 0.8115 | 0.9657   |
+| 0.0725        | 3.33  | 1500 | 0.1444          | 0.7953    | 0.8625 | 0.8275 | 0.9668   |
+| 0.0492        | 4.44  | 2000 | 0.1513          | 0.8085    | 0.8760 | 0.8409 | 0.9675   |
+| 0.0388        | 5.56  | 2500 | 0.1604          | 0.8257    | 0.8731 | 0.8487 | 0.9674   |
+| 0.0244        | 6.67  | 3000 | 0.1754          | 0.8278    | 0.8629 | 0.8450 | 0.9666   |
+| 0.0169        | 7.78  | 3500 | 0.1877          | 0.8282    | 0.8653 | 0.8464 | 0.9677   |
+| 0.0102        | 8.89  | 4000 | 0.1974          | 0.8252    | 0.8634 | 0.8439 | 0.9674   |
+| 0.0068        | 10.0  | 4500 | 0.2072          | 0.8317    | 0.8682 | 0.8496 | 0.9680   |
+
+
+### Framework versions
+
+- Transformers 4.36.2
+- Pytorch 2.1.2+cu121
+- Datasets 2.16.1
+- Tokenizers 0.15.0