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_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: 0.8298668885191348
+    - name: Recall
+      type: recall
+      value: 0.8807947019867549
+    - name: F1
+      type: f1
+      value: 0.8545727136431784
+    - name: Accuracy
+      type: accuracy
+      value: 0.9612006713767126
+---
+
+<!-- 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_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: 0.2279
+- Precision: 0.8299
+- Recall: 0.8808
+- F1: 0.8546
+- Accuracy: 0.9612
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.5393        | 1.7   | 500  | 0.2155          | 0.7523    | 0.8313 | 0.7898 | 0.9494   |
+| 0.164         | 3.4   | 1000 | 0.1931          | 0.7883    | 0.8481 | 0.8171 | 0.9563   |
+| 0.0958        | 5.1   | 1500 | 0.2049          | 0.8092    | 0.8614 | 0.8345 | 0.9587   |
+| 0.0523        | 6.8   | 2000 | 0.2152          | 0.8265    | 0.8728 | 0.8490 | 0.9595   |
+| 0.0323        | 8.5   | 2500 | 0.2279          | 0.8299    | 0.8808 | 0.8546 | 0.9612   |
+
+
+### Framework versions
+
+- Transformers 4.36.2
+- Pytorch 2.1.2+cu121
+- Datasets 2.16.1
+- Tokenizers 0.15.0

runs/Mar04_23-10-18_g11/events.out.tfevents.1709590223.g11.840720.0

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