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# jq_emo_distilbert

This model is a fine-tuned version of [tingtone/jq_emo_distilbert](https://huggingface.co/tingtone/jq_emo_distilbert) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3185
- Accuracy: 0.9385

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 16000
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1042        | 1.0   | 1000  | 0.1816          | 0.932    |
| 0.0998        | 2.0   | 2000  | 0.1799          | 0.934    |
| 0.0957        | 3.0   | 3000  | 0.2015          | 0.935    |
| 0.0846        | 4.0   | 4000  | 0.2129          | 0.9335   |
| 0.0943        | 5.0   | 5000  | 0.2215          | 0.935    |
| 0.075         | 6.0   | 6000  | 0.2627          | 0.9375   |
| 0.0607        | 7.0   | 7000  | 0.2908          | 0.9345   |
| 0.0636        | 8.0   | 8000  | 0.3207          | 0.935    |
| 0.0953        | 9.0   | 9000  | 0.3165          | 0.936    |
| 0.0748        | 10.0  | 10000 | 0.3185          | 0.9385   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy"], "model-index": [{"name": "jq_emo_distilbert", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "config": "split", "split": "validation", "args": "split"}, "metrics": [{"type": "accuracy", "value": 0.9385, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard distilbert text-classification generated_from_trainer dataset:emotion license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

jq\_emo\_distilbert
===================


This model is a fine-tuned version of tingtone/jq\_emo\_distilbert on the emotion dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3185
* Accuracy: 0.9385


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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 16000
* num\_epochs: 10


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 16000
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 16000
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3