File size: 2,413 Bytes
290fa1f 5d9a47c 290fa1f 5d9a47c 290fa1f 5d9a47c 290fa1f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | ---
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: urgency_classifier
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. -->
# urgency_classifier
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0258
- Accuracy: 0.9187
- F1 Macro: 0.9201
- F1 Weighted: 0.9185
- Precision Macro: 0.9203
- Recall Macro: 0.9221
- Precision Weighted: 0.9204
- Recall Weighted: 0.9187
## 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: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Weighted | Precision Macro | Recall Macro | Precision Weighted | Recall Weighted |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------------:|:------------:|:------------------:|:---------------:|
| 0.0969 | 0.625 | 50 | 0.0419 | 0.7625 | 0.7566 | 0.7447 | 0.7724 | 0.7877 | 0.7773 | 0.7625 |
| 0.031 | 1.25 | 100 | 0.0272 | 0.8688 | 0.8704 | 0.8658 | 0.8697 | 0.8804 | 0.8725 | 0.8688 |
| 0.0202 | 1.875 | 150 | 0.0232 | 0.9 | 0.9022 | 0.8993 | 0.9015 | 0.9060 | 0.9015 | 0.9 |
| 0.0132 | 2.5 | 200 | 0.0258 | 0.9187 | 0.9201 | 0.9185 | 0.9203 | 0.9221 | 0.9204 | 0.9187 |
### Framework versions
- Transformers 4.57.1
- Pytorch 2.9.0+cu128
- Datasets 4.3.0
- Tokenizers 0.22.1
|