
# distilbert-base-uncased-go-emotions-student

## Model Description

This model is distilled from the zero-shot classification pipeline on the unlabeled GoEmotions dataset using [this
script](https://github.com/huggingface/transformers/tree/master/examples/research_projects/zero-shot-distillation).
It was trained with mixed precision for 10 epochs and otherwise used the default script arguments. 

## Intended Usage

The model can be used like any other model trained on GoEmotions, but will likely not perform as well as a model
trained with full supervision. It is primarily intended as a demo of how an expensive NLI-based zero-shot model
can be distilled to a more efficient student, allowing a classifier to be trained with only unlabeled data. Note
that although the GoEmotions dataset allow multiple labels per instance, the teacher used single-label 
classification to create psuedo-labels.

{"language": "en", "license": "mit", "tags": ["text-classification", "pytorch", "tensorflow"], "datasets": ["go_emotions"], "widget": [{"text": "I feel lucky to be here."}]}
transformers pytorch tf distilbert text-classification tensorflow en dataset:go_emotions license:mit autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tf #distilbert #text-classification #tensorflow #en #dataset-go_emotions #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 


# distilbert-base-uncased-go-emotions-student

## Model Description

This model is distilled from the zero-shot classification pipeline on the unlabeled GoEmotions dataset using this
script.
It was trained with mixed precision for 10 epochs and otherwise used the default script arguments. 

## Intended Usage

The model can be used like any other model trained on GoEmotions, but will likely not perform as well as a model
trained with full supervision. It is primarily intended as a demo of how an expensive NLI-based zero-shot model
can be distilled to a more efficient student, allowing a classifier to be trained with only unlabeled data. Note
that although the GoEmotions dataset allow multiple labels per instance, the teacher used single-label 
classification to create psuedo-labels.

# distilbert-base-uncased-go-emotions-student ## Model Description

This model is distilled from the zero-shot classification pipeline on the unlabeled GoEmotions dataset using this
script.
It was trained with mixed precision for 10 epochs and otherwise used the default script arguments. ## Intended Usage

The model can be used like any other model trained on GoEmotions, but will likely not perform as well as a model
trained with full supervision. It is primarily intended as a demo of how an expensive NLI-based zero-shot model
can be distilled to a more efficient student, allowing a classifier to be trained with only unlabeled data. Note
that although the GoEmotions dataset allow multiple labels per instance, the teacher used single-label 
classification to create psuedo-labels.
TAGS
#transformers #pytorch #tf #distilbert #text-classification #tensorflow #en #dataset-go_emotions #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # distilbert-base-uncased-go-emotions-student ## Model Description

This model is distilled from the zero-shot classification pipeline on the unlabeled GoEmotions dataset using this
script.
It was trained with mixed precision for 10 epochs and otherwise used the default script arguments. ## Intended Usage

The model can be used like any other model trained on GoEmotions, but will likely not perform as well as a model
trained with full supervision. It is primarily intended as a demo of how an expensive NLI-based zero-shot model
can be distilled to a more efficient student, allowing a classifier to be trained with only unlabeled data. Note
that although the GoEmotions dataset allow multiple labels per instance, the teacher used single-label 
classification to create psuedo-labels.
passage: TAGS
#transformers #pytorch #tf #distilbert #text-classification #tensorflow #en #dataset-go_emotions #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
# distilbert-base-uncased-go-emotions-student## Model Description

This model is distilled from the zero-shot classification pipeline on the unlabeled GoEmotions dataset using this
script.
It was trained with mixed precision for 10 epochs and otherwise used the default script arguments.## Intended Usage

The model can be used like any other model trained on GoEmotions, but will likely not perform as well as a model
trained with full supervision. It is primarily intended as a demo of how an expensive NLI-based zero-shot model
can be distilled to a more efficient student, allowing a classifier to be trained with only unlabeled data. Note
that although the GoEmotions dataset allow multiple labels per instance, the teacher used single-label 
classification to create psuedo-labels.