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-PO-MSV-D2-data-AmazonScience-massive-all_1.1
  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. -->

# scenario-KD-PO-MSV-D2-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: 1.1344
- Accuracy: 0.8576
- F1: 0.8356

## 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: 44
- 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     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.982         | 0.27  | 5000  | 1.5102          | 0.8376   | 0.8072 |
| 0.8116        | 0.53  | 10000 | 1.4268          | 0.8422   | 0.8190 |
| 0.7139        | 0.8   | 15000 | 1.3857          | 0.8455   | 0.8258 |
| 0.5581        | 1.07  | 20000 | 1.3504          | 0.8475   | 0.8237 |
| 0.5406        | 1.34  | 25000 | 1.3204          | 0.8504   | 0.8242 |
| 0.5278        | 1.6   | 30000 | 1.3101          | 0.8502   | 0.8258 |
| 0.5039        | 1.87  | 35000 | 1.2879          | 0.8511   | 0.8263 |
| 0.4456        | 2.14  | 40000 | 1.2609          | 0.8515   | 0.8279 |
| 0.4241        | 2.41  | 45000 | 1.2388          | 0.8536   | 0.8278 |
| 0.4072        | 2.67  | 50000 | 1.2111          | 0.8547   | 0.8309 |
| 0.4034        | 2.94  | 55000 | 1.2163          | 0.8529   | 0.8290 |
| 0.3609        | 3.21  | 60000 | 1.1896          | 0.8537   | 0.8307 |
| 0.3584        | 3.47  | 65000 | 1.1668          | 0.8558   | 0.8333 |
| 0.3493        | 3.74  | 70000 | 1.1638          | 0.8552   | 0.8313 |
| 0.3415        | 4.01  | 75000 | 1.1481          | 0.8573   | 0.8319 |
| 0.3128        | 4.28  | 80000 | 1.1441          | 0.8565   | 0.8336 |
| 0.3159        | 4.54  | 85000 | 1.1397          | 0.8569   | 0.8339 |
| 0.3144        | 4.81  | 90000 | 1.1344          | 0.8576   | 0.8356 |


### Framework versions

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