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---
license: mit
base_model: facebook/xlm-v-base
tags:
- generated_from_trainer
datasets:
- massive
metrics:
- accuracy
- f1
model-index:
- name: scenario-TCR-XLMV_data-AmazonScience_massive_all_1_1_beta2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: massive
type: massive
config: all_1.1
split: validation
args: all_1.1
metrics:
- name: Accuracy
type: accuracy
value: 0.8495213591130955
- name: F1
type: f1
value: 0.8257523979629272
---
<!-- 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. -->
# scenario-TCR-XLMV_data-AmazonScience_massive_all_1_1_beta2
This model is a fine-tuned version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8678
- Accuracy: 0.8495
- F1: 0.8258
## 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: 32
- eval_batch_size: 32
- seed: 67
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|
| 0.6252 | 0.27 | 5000 | 0.7387 | 0.8183 | 0.7743 |
| 0.4497 | 0.53 | 10000 | 0.6721 | 0.8363 | 0.7908 |
| 0.3806 | 0.8 | 15000 | 0.6702 | 0.8451 | 0.8090 |
| 0.303 | 1.07 | 20000 | 0.7162 | 0.8457 | 0.8130 |
| 0.2732 | 1.34 | 25000 | 0.7250 | 0.8475 | 0.8178 |
| 0.2574 | 1.6 | 30000 | 0.7626 | 0.8449 | 0.8188 |
| 0.2565 | 1.87 | 35000 | 0.7255 | 0.8506 | 0.8251 |
| 0.2074 | 2.14 | 40000 | 0.7439 | 0.8524 | 0.8268 |
| 0.2139 | 2.41 | 45000 | 0.8088 | 0.8478 | 0.8233 |
| 0.2007 | 2.67 | 50000 | 0.7556 | 0.8476 | 0.8223 |
| 0.2012 | 2.94 | 55000 | 0.7599 | 0.8505 | 0.8250 |
| 0.1698 | 3.21 | 60000 | 0.8283 | 0.8481 | 0.8255 |
| 0.1728 | 3.47 | 65000 | 0.7996 | 0.8521 | 0.8320 |
| 0.1711 | 3.74 | 70000 | 0.7974 | 0.8520 | 0.8292 |
| 0.1623 | 4.01 | 75000 | 0.8819 | 0.8485 | 0.8223 |
| 0.1502 | 4.28 | 80000 | 0.8330 | 0.8534 | 0.8320 |
| 0.1605 | 4.54 | 85000 | 0.8250 | 0.8499 | 0.8264 |
| 0.1659 | 4.81 | 90000 | 0.8318 | 0.8493 | 0.8237 |
| 0.1241 | 5.08 | 95000 | 0.9368 | 0.8518 | 0.8191 |
| 0.1361 | 5.34 | 100000 | 0.9396 | 0.8510 | 0.8237 |
| 0.1481 | 5.61 | 105000 | 0.8678 | 0.8495 | 0.8258 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
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