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_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: 0.848714069591528
+    - name: Recall
+      type: recall
+      value: 0.8995189738107964
+    - name: F1
+      type: f1
+      value: 0.8733783082511676
+    - name: Accuracy
+      type: accuracy
+      value: 0.9711435696473103
+---
+
+<!-- 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_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: 0.1689
+- Precision: 0.8487
+- Recall: 0.8995
+- F1: 0.8734
+- Accuracy: 0.9711
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.3372        | 1.72  | 500  | 0.1525          | 0.7806    | 0.8632 | 0.8198 | 0.9639   |
+| 0.117         | 3.44  | 1000 | 0.1341          | 0.8162    | 0.8899 | 0.8514 | 0.9702   |
+| 0.077         | 5.15  | 1500 | 0.1457          | 0.8204    | 0.8765 | 0.8475 | 0.9672   |
+| 0.0548        | 6.87  | 2000 | 0.1759          | 0.8449    | 0.8910 | 0.8673 | 0.9690   |
+| 0.037         | 8.59  | 2500 | 0.1689          | 0.8487    | 0.8995 | 0.8734 | 0.9711   |
+
+
+### Framework versions
+
+- Transformers 4.36.2
+- Pytorch 2.1.2+cu121
+- Datasets 2.16.1
+- Tokenizers 0.15.0

runs/Mar05_14-10-17_g11/events.out.tfevents.1709644219.g11.949095.0

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