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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: jq_emo_gpt
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.939
---
<!-- 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. -->
# jq_emo_gpt
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2836
- Accuracy: 0.939
## 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: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 6400
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5336 | 1.0 | 16000 | 0.6176 | 0.9215 |
| 0.2962 | 2.0 | 32000 | 0.3453 | 0.9375 |
| 0.3416 | 3.0 | 48000 | 0.3061 | 0.938 |
| 0.1796 | 4.0 | 64000 | 0.2836 | 0.939 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|