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-DIV2-1000-data-AmazonScience-massive-all_1.1
  results: []
---

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# scenario-KD-POST-DIV2-1000-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: 2.9496
- Accuracy: 0.7382
- F1: 0.6644

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.56  | 50   | 3.3279          | 0.7159   | 0.6377 |
| No log        | 3.12  | 100  | 3.0342          | 0.7349   | 0.6632 |
| No log        | 4.69  | 150  | 2.9496          | 0.7382   | 0.6644 |


### Framework versions

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