
# Dynamically quantized DistilBERT base uncased finetuned SST-2

## Table of Contents
- [Model Details](#model-details)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)

## Model Details
**Model Description:** This model is a [DistilBERT](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) fine-tuned on SST-2 dynamically quantized with [optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [Intel® Neural Compressor](https://github.com/intel/neural-compressor).
- **Model Type:** Text Classification
- **Language(s):** English
- **License:** Apache-2.0
- **Parent Model:** For more details on the original model, we encourage users to check out [this](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) model card.

## How to Get Started With the Model

This requires to install Optimum :
`pip install optimum[neural-compressor]`

To load the quantized model and run inference using the Transformers [pipelines](https://huggingface.co/docs/transformers/main/en/main_classes/pipelines), you can do as follows:

```python
from transformers import AutoTokenizer, pipeline
from optimum.intel import INCModelForSequenceClassification

model_id = "echarlaix/distilbert-base-uncased-finetuned-sst-2-english-int8-dynamic"
model = INCModelForSequenceClassification.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
cls_pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "He's a dreadful magician."
outputs = cls_pipe(text)
```

{"language": "en", "license": "apache-2.0", "tags": ["text-classification", "neural-compressor", "int8", "8-bit"], "datasets": ["sst2", "glue"], "metrics": ["accuracy"]}
transformers pytorch distilbert text-classification neural-compressor int8 8-bit en dataset:sst2 dataset:glue license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #text-classification #neural-compressor #int8 #8-bit #en #dataset-sst2 #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


# Dynamically quantized DistilBERT base uncased finetuned SST-2

## Table of Contents
- Model Details
- How to Get Started With the Model

## Model Details
Model Description: This model is a DistilBERT fine-tuned on SST-2 dynamically quantized with optimum-intel through the usage of Intel® Neural Compressor.
- Model Type: Text Classification
- Language(s): English
- License: Apache-2.0
- Parent Model: For more details on the original model, we encourage users to check out this model card.

## How to Get Started With the Model

This requires to install Optimum :
'pip install optimum[neural-compressor]'

To load the quantized model and run inference using the Transformers pipelines, you can do as follows:



# Dynamically quantized DistilBERT base uncased finetuned SST-2 ## Table of Contents
- Model Details
- How to Get Started With the Model ## Model Details
Model Description: This model is a DistilBERT fine-tuned on SST-2 dynamically quantized with optimum-intel through the usage of Intel® Neural Compressor.
- Model Type: Text Classification
- Language(s): English
- License: Apache-2.0
- Parent Model: For more details on the original model, we encourage users to check out this model card. ## How to Get Started With the Model

This requires to install Optimum :
'pip install optimum[neural-compressor]'

To load the quantized model and run inference using the Transformers pipelines, you can do as follows:
TAGS
#transformers #pytorch #distilbert #text-classification #neural-compressor #int8 #8-bit #en #dataset-sst2 #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # Dynamically quantized DistilBERT base uncased finetuned SST-2 ## Table of Contents
- Model Details
- How to Get Started With the Model ## Model Details
Model Description: This model is a DistilBERT fine-tuned on SST-2 dynamically quantized with optimum-intel through the usage of Intel® Neural Compressor.
- Model Type: Text Classification
- Language(s): English
- License: Apache-2.0
- Parent Model: For more details on the original model, we encourage users to check out this model card. ## How to Get Started With the Model

This requires to install Optimum :
'pip install optimum[neural-compressor]'

To load the quantized model and run inference using the Transformers pipelines, you can do as follows:
passage: TAGS
#transformers #pytorch #distilbert #text-classification #neural-compressor #int8 #8-bit #en #dataset-sst2 #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# Dynamically quantized DistilBERT base uncased finetuned SST-2## Table of Contents
- Model Details
- How to Get Started With the Model## Model Details
Model Description: This model is a DistilBERT fine-tuned on SST-2 dynamically quantized with optimum-intel through the usage of Intel® Neural Compressor.
- Model Type: Text Classification
- Language(s): English
- License: Apache-2.0
- Parent Model: For more details on the original model, we encourage users to check out this model card.## How to Get Started With the Model

This requires to install Optimum :
'pip install optimum[neural-compressor]'

To load the quantized model and run inference using the Transformers pipelines, you can do as follows: