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-DIV2-100000-data-AmazonScience-massive-all_1.1-model-haryoaw-sc
  results: []
---

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# scenario-KD-POST-DIV2-100000-data-AmazonScience-massive-all_1.1-model-haryoaw-sc

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: 1.3714
- Accuracy: 0.8464
- F1: 0.8187

## 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     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.7862        | 1.6   | 5000  | 1.5297          | 0.8375   | 0.8035 |
| 0.5095        | 3.2   | 10000 | 1.4226          | 0.8453   | 0.8166 |
| 0.4424        | 4.8   | 15000 | 1.3714          | 0.8464   | 0.8187 |


### Framework versions

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