# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

This is a BERT model fine-tuned on [multi_nli](https://huggingface.co/datasets/multi_nli) dataset.

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->


- **Developed by:** nouf-sst.
- **Model type:** Transformer.
- **Language(s) (NLP):** English.
- **Finetuned from model [optional]:** BERT.


## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

The model is finetuned on subset of multi_nli dataset (i.e., 50000 samples).


## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[More Information Needed]

### Training Procedure 

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Data Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary


## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]




{"language": ["en"], "library_name": "transformers", "tags": ["multi-nli", "bert"], "pipeline_tag": "text-classification"}
transformers pytorch bert text-classification multi-nli en autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #bert #text-classification #multi-nli #en #autotrain_compatible #endpoints_compatible #has_space #region-us 

# Model Card for Model ID



This is a BERT model fine-tuned on multi_nli dataset.

## Model Details

### Model Description




- Developed by: nouf-sst.
- Model type: Transformer.
- Language(s) (NLP): English.
- Finetuned from model [optional]: BERT.


## Bias, Risks, and Limitations



The model is finetuned on subset of multi_nli dataset (i.e., 50000 samples).


## How to Get Started with the Model

Use the code below to get started with the model.



## Training Details

### Training Data





### Training Procedure 



#### Preprocessing [optional]




#### Training Hyperparameters

- Training regime:  

#### Speeds, Sizes, Times [optional]





## Evaluation



### Testing Data, Factors & Metrics

#### Testing Data





#### Factors





#### Metrics





### Results



#### Summary


[optional]



BibTeX:



APA:






# Model Card for Model ID



This is a BERT model fine-tuned on multi_nli dataset. ## Model Details ### Model Description




- Developed by: nouf-sst.
- Model type: Transformer.
- Language(s) (NLP): English.
- Finetuned from model [optional]: BERT. ## Bias, Risks, and Limitations



The model is finetuned on subset of multi_nli dataset (i.e., 50000 samples). ## How to Get Started with the Model

Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters

- Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary


[optional]



BibTeX:



APA:
TAGS
#transformers #pytorch #bert #text-classification #multi-nli #en #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # Model Card for Model ID



This is a BERT model fine-tuned on multi_nli dataset. ## Model Details ### Model Description




- Developed by: nouf-sst.
- Model type: Transformer.
- Language(s) (NLP): English.
- Finetuned from model [optional]: BERT. ## Bias, Risks, and Limitations



The model is finetuned on subset of multi_nli dataset (i.e., 50000 samples). ## How to Get Started with the Model

Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters

- Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary


[optional]



BibTeX:



APA:
TAGS
#transformers #pytorch #bert #text-classification #multi-nli #en #autotrain_compatible #endpoints_compatible #has_space #region-us 
# Model Card for Model ID



This is a BERT model fine-tuned on multi_nli dataset.## Model Details### Model Description




- Developed by: nouf-sst.
- Model type: Transformer.
- Language(s) (NLP): English.
- Finetuned from model [optional]: BERT.## Bias, Risks, and Limitations



The model is finetuned on subset of multi_nli dataset (i.e., 50000 samples).## How to Get Started with the Model

Use the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters

- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary


[optional]



BibTeX:



APA: