classify-consumer

.gitattributes

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README.md

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+---
+license: mit
+base_model: FacebookAI/xlm-roberta-base
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: training_dir
+  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. -->
+
+# training_dir
+
+This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4296
+- Accuracy: 0.8650
+- F1: 0.8647
+- Precision: 0.8662
+- Recall: 0.8650
+- Accuracy Label Communication Issue: 0.3878
+- Accuracy Label General Query: 0.5135
+- Accuracy Label Other: 0.8535
+- Accuracy Label Praise: 0.7987
+- Accuracy Label Service Issue: 0.9503
+
+## 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: 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: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Accuracy Label Communication Issue | Accuracy Label General Query | Accuracy Label Other | Accuracy Label Praise | Accuracy Label Service Issue |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:----------------------------------:|:----------------------------:|:--------------------:|:---------------------:|:----------------------------:|
+| 0.4926        | 1.0   | 220  | 0.4769          | 0.8351   | 0.7977 | 0.7654    | 0.8351 | 0.0                                | 0.0                          | 0.8479               | 0.7315                | 0.9780                       |
+| 0.4722        | 2.0   | 440  | 0.4490          | 0.8138   | 0.8335 | 0.8685    | 0.8138 | 0.5918                             | 0.2973                       | 0.8592               | 0.8523                | 0.8358                       |
+| 0.1949        | 3.0   | 660  | 0.4296          | 0.8650   | 0.8647 | 0.8662    | 0.8650 | 0.3878                             | 0.5135                       | 0.8535               | 0.7987                | 0.9503                       |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

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