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: CNEC1_1_62types_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: 0.8078956644342615
+    - name: Recall
+      type: recall
+      value: 0.8744753910721099
+    - name: F1
+      type: f1
+      value: 0.8398680835470869
+    - name: Accuracy
+      type: accuracy
+      value: 0.9517155236348023
+---
+
+<!-- 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. -->
+
+# CNEC1_1_62types_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: 0.2443
+- Precision: 0.8079
+- Recall: 0.8745
+- F1: 0.8399
+- Accuracy: 0.9517
+
+## 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
+- lr_scheduler_warmup_ratio: 0.1
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 1.3073        | 1.7   | 500  | 0.3156          | 0.6691    | 0.7375 | 0.7016 | 0.9264   |
+| 0.2766        | 3.4   | 1000 | 0.2292          | 0.7654    | 0.8329 | 0.7977 | 0.9472   |
+| 0.167         | 5.1   | 1500 | 0.2255          | 0.7762    | 0.8523 | 0.8125 | 0.9498   |
+| 0.1066        | 6.8   | 2000 | 0.2431          | 0.7818    | 0.8573 | 0.8178 | 0.9492   |
+| 0.0752        | 8.5   | 2500 | 0.2443          | 0.8079    | 0.8745 | 0.8399 | 0.9517   |
+
+
+### Framework versions
+
+- Transformers 4.36.2
+- Pytorch 2.1.2+cu121
+- Datasets 2.16.1
+- Tokenizers 0.15.0

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