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---
license: mit
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
base_model: gpt2
model-index:
- name: jq_emo_gpt
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- type: accuracy
value: 0.947
name: Accuracy
---
<!-- 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 [gpt2](https://huggingface.co/gpt2) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2536
- Accuracy: 0.947
## 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: 64
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5109 | 1.0 | 16000 | 0.5014 | 0.929 |
| 0.3765 | 2.0 | 32000 | 0.3135 | 0.9385 |
| 0.2526 | 3.0 | 48000 | 0.2385 | 0.945 |
| 0.1952 | 4.0 | 64000 | 0.2536 | 0.947 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|