transformers pytorch xlm-roberta generated_from_trainer dataset:massive base_model:haryoaw/scenario-TCR-data-AmazonScience-massive-all_1.1-model-xlm-roberta-base license:mit endpoints_compatible region:us
---
license: mit
base_model: haryoaw/scenario-TCR-data-AmazonScience-massive-all_1.1-model-xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- massive
metrics:
- accuracy
- f1
model-index:
- name: scenario-KD-POST-DIV3-data-AmazonScience-massive-all_1.1
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# scenario-KD-POST-DIV3-data-AmazonScience-massive-all_1.1

This model is a fine-tuned version of [haryoaw/scenario-TCR-data-AmazonScience-massive-all_1.1-model-xlm-roberta-base](https://huggingface.co/haryoaw/scenario-TCR-data-AmazonScience-massive-all_1.1-model-xlm-roberta-base) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2891
- Accuracy: 0.8521
- F1: 0.8302

## 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: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 1.5612        | 0.27  | 5000  | 1.8162          | 0.8142   | 0.7801 |
| 1.2203        | 0.53  | 10000 | 1.6261          | 0.8275   | 0.7956 |
| 1.0671        | 0.8   | 15000 | 1.5042          | 0.8380   | 0.8047 |
| 0.8104        | 1.07  | 20000 | 1.4886          | 0.8394   | 0.8087 |
| 0.7814        | 1.34  | 25000 | 1.4688          | 0.8413   | 0.8186 |
| 0.7174        | 1.6   | 30000 | 1.4204          | 0.8438   | 0.8191 |
| 0.6924        | 1.87  | 35000 | 1.4041          | 0.8424   | 0.8200 |
| 0.5952        | 2.14  | 40000 | 1.4070          | 0.8427   | 0.8218 |
| 0.5615        | 2.41  | 45000 | 1.3820          | 0.8472   | 0.8221 |
| 0.5524        | 2.67  | 50000 | 1.3527          | 0.8485   | 0.8262 |
| 0.5346        | 2.94  | 55000 | 1.3407          | 0.8481   | 0.8256 |
| 0.4604        | 3.21  | 60000 | 1.3444          | 0.8478   | 0.8257 |
| 0.4758        | 3.47  | 65000 | 1.3268          | 0.8505   | 0.8294 |
| 0.4421        | 3.74  | 70000 | 1.3246          | 0.8500   | 0.8301 |
| 0.4444        | 4.01  | 75000 | 1.3035          | 0.8504   | 0.8300 |
| 0.4141        | 4.28  | 80000 | 1.2987          | 0.8517   | 0.8289 |
| 0.407         | 4.54  | 85000 | 1.2926          | 0.8517   | 0.8293 |
| 0.3999        | 4.81  | 90000 | 1.2891          | 0.8521   | 0.8302 |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
