transformers pytorch xlm-roberta generated_from_trainer dataset:massive base_model:haryoaw/scenario-TCR-data-cl-massive-all_1.1 license:mit endpoints_compatible region:us
---
license: mit
base_model: haryoaw/scenario-TCR-data-cl-massive-all_1.1
tags:
- generated_from_trainer
datasets:
- massive
metrics:
- accuracy
- f1
model-index:
- name: scenario-NON-KD-PO-COPY-D2_data-cl-massive_all_1_1
  results: []
---

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# scenario-NON-KD-PO-COPY-D2_data-cl-massive_all_1_1

This model is a fine-tuned version of [haryoaw/scenario-TCR-data-cl-massive-all_1.1](https://huggingface.co/haryoaw/scenario-TCR-data-cl-massive-all_1.1) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7405
- Accuracy: 0.7219
- F1: 0.6699

## 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     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.3557        | 0.56  | 5000  | 1.2187          | 0.7147   | 0.6609 |
| 0.2056        | 1.11  | 10000 | 1.3617          | 0.7087   | 0.6532 |
| 0.1869        | 1.67  | 15000 | 1.4154          | 0.7095   | 0.6590 |
| 0.1216        | 2.22  | 20000 | 1.5366          | 0.7114   | 0.6606 |
| 0.1172        | 2.78  | 25000 | 1.5643          | 0.7144   | 0.6669 |
| 0.0726        | 3.33  | 30000 | 1.6502          | 0.7194   | 0.6671 |
| 0.069         | 3.89  | 35000 | 1.6554          | 0.7224   | 0.6708 |
| 0.0463        | 4.45  | 40000 | 1.7405          | 0.7219   | 0.6699 |


### Framework versions

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