# Model Description

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) and fine-tuning it on the
[github ticket tagger dataset](https://tickettagger.blob.core.windows.net/datasets/dataset-labels-top3-30k-real.txt). It classifies issue into 3 common categories: Bug, Enhancement, Questions.

It achieves the following results on the evaluation set:
- Accuracy: 0.7862

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-5
- train_batch_size: 16
- optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0
- num_epochs: 5
### Codes
https://github.com/IvanLauLinTiong/IntelliLabel
{"datasets": ["ticket-tagger"], "metrics": ["accuracy"], "model-index": [{"name": "distil-bert-uncased-finetuned-github-issues", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "ticket tagger", "type": "ticket tagger", "args": "full"}, "metrics": [{"type": "accuracy", "value": 0.7862, "name": "Accuracy"}]}]}]}
transformers pytorch distilbert text-classification dataset:ticket-tagger autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #distilbert #text-classification #dataset-ticket-tagger #autotrain_compatible #endpoints_compatible #has_space #region-us 

# Model Description

This model is a fine-tuned version of distilbert-base-uncased and fine-tuning it on the
github ticket tagger dataset. It classifies issue into 3 common categories: Bug, Enhancement, Questions.

It achieves the following results on the evaluation set:
- Accuracy: 0.7862

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-5
- train_batch_size: 16
- optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0
- num_epochs: 5
### Codes
URL
# Model Description

This model is a fine-tuned version of distilbert-base-uncased and fine-tuning it on the
github ticket tagger dataset. It classifies issue into 3 common categories: Bug, Enhancement, Questions.

It achieves the following results on the evaluation set:
- Accuracy: 0.7862 ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-5
- train_batch_size: 16
- optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0
- num_epochs: 5 ### Codes
URL
TAGS
#transformers #pytorch #distilbert #text-classification #dataset-ticket-tagger #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # Model Description

This model is a fine-tuned version of distilbert-base-uncased and fine-tuning it on the
github ticket tagger dataset. It classifies issue into 3 common categories: Bug, Enhancement, Questions.

It achieves the following results on the evaluation set:
- Accuracy: 0.7862 ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-5
- train_batch_size: 16
- optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0
- num_epochs: 5 ### Codes
URL
passage: TAGS
#transformers #pytorch #distilbert #text-classification #dataset-ticket-tagger #autotrain_compatible #endpoints_compatible #has_space #region-us 
# Model Description

This model is a fine-tuned version of distilbert-base-uncased and fine-tuning it on the
github ticket tagger dataset. It classifies issue into 3 common categories: Bug, Enhancement, Questions.

It achieves the following results on the evaluation set:
- Accuracy: 0.7862### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-5
- train_batch_size: 16
- optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0
- num_epochs: 5### Codes
URL