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-NON-KD-PO-COPY-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
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# scenario-NON-KD-PO-COPY-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: 1.0024
- Accuracy: 0.8568
- F1: 0.8337

## 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: 47
- 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     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.3036        | 0.27  | 5000  | 0.6755          | 0.8385   | 0.8113 |
| 0.2572        | 0.53  | 10000 | 0.6656          | 0.8478   | 0.8227 |
| 0.2277        | 0.8   | 15000 | 0.7029          | 0.8441   | 0.8169 |
| 0.1409        | 1.07  | 20000 | 0.7635          | 0.8484   | 0.8242 |
| 0.1488        | 1.34  | 25000 | 0.7725          | 0.8474   | 0.8215 |
| 0.1543        | 1.6   | 30000 | 0.7682          | 0.8508   | 0.8278 |
| 0.139         | 1.87  | 35000 | 0.7771          | 0.8533   | 0.8254 |
| 0.0957        | 2.14  | 40000 | 0.8306          | 0.8547   | 0.8317 |
| 0.1029        | 2.41  | 45000 | 0.8558          | 0.8507   | 0.8272 |
| 0.0956        | 2.67  | 50000 | 0.8828          | 0.8506   | 0.8252 |
| 0.0869        | 2.94  | 55000 | 0.8841          | 0.8551   | 0.8348 |
| 0.0604        | 3.21  | 60000 | 0.9202          | 0.8546   | 0.8337 |
| 0.0676        | 3.47  | 65000 | 0.9465          | 0.8544   | 0.8304 |
| 0.0566        | 3.74  | 70000 | 0.9452          | 0.8567   | 0.8344 |
| 0.0529        | 4.01  | 75000 | 0.9742          | 0.8531   | 0.8299 |
| 0.0386        | 4.28  | 80000 | 1.0027          | 0.8554   | 0.8337 |
| 0.0392        | 4.54  | 85000 | 1.0100          | 0.8552   | 0.8323 |
| 0.0381        | 4.81  | 90000 | 1.0024          | 0.8568   | 0.8337 |


### Framework versions

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