adriansanz/intent_analysis_xml_5ep_v1_es_ca

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
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+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,71 @@
+---
+library_name: transformers
+license: mit
+base_model: xlm-roberta-base
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: intent_analysis_V1_TOTAL
+  results: []
+---
+
+<!-- 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. -->
+
+# intent_analysis_V1_TOTAL
+
+This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0166
+- Accuracy: 0.9971
+- Precision: 0.9971
+- Recall: 0.9971
+- F1: 0.9971
+
+## 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: 5e-05
+- train_batch_size: 64
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| No log        | 1.0   | 214  | 0.0492          | 0.9875   | 0.9875    | 0.9875 | 0.9875 |
+| No log        | 2.0   | 428  | 0.0385          | 0.9899   | 0.9901    | 0.9899 | 0.9899 |
+| 0.6012        | 3.0   | 642  | 0.0265          | 0.9935   | 0.9935    | 0.9935 | 0.9935 |
+| 0.6012        | 4.0   | 856  | 0.0199          | 0.9966   | 0.9966    | 0.9966 | 0.9966 |
+| 0.0105        | 5.0   | 1070 | 0.0166          | 0.9971   | 0.9971    | 0.9971 | 0.9971 |
+
+
+### Framework versions
+
+- Transformers 4.46.2
+- Pytorch 2.5.0+cu121
+- Datasets 3.1.0
+- Tokenizers 0.20.3

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special_tokens_map.json

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+}

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