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# Neuron conversation

# MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-distilled-clinc

This model is a fine-tuned version of [nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large](https://huggingface.co/nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.9389999

## Deploy/use Model

If you want to use this model checkout the following notenbook: [sagemaker/18_inferentia_inference](https://github.com/huggingface/notebooks/blob/main/sagemaker/18_inferentia_inference/sagemaker-notebook.ipynb)

```python
from sagemaker.huggingface.model import HuggingFaceModel


# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
   model_data=s3_model_uri,       # path to your model and script
   role=role,                    # iam role with permissions to create an Endpoint
   transformers_version="4.12",  # transformers version used
   pytorch_version="1.9",        # pytorch version used
   py_version='py37',            # python version used
)

# Let SageMaker know that we've already compiled the model via neuron-cc
huggingface_model._is_compiled_model = True

# deploy the endpoint endpoint
predictor = huggingface_model.deploy(
    initial_instance_count=1,      # number of instances
    instance_type="ml.inf1.xlarge" # AWS Inferentia Instance
)
```
{"tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-distilled-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos", "args": "plus"}, "metrics": [{"type": "accuracy", "value": 0.94, "name": "Accuracy"}]}]}]}
transformers roberta text-classification generated_from_trainer dataset:clinc_oos model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #roberta #text-classification #generated_from_trainer #dataset-clinc_oos #model-index #autotrain_compatible #endpoints_compatible #region-us 



# Neuron conversation

# MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-distilled-clinc

This model is a fine-tuned version of nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.9389999

## Deploy/use Model

If you want to use this model checkout the following notenbook: sagemaker/18_inferentia_inference


# Neuron conversation # MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-distilled-clinc

This model is a fine-tuned version of nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.9389999 ## Deploy/use Model

If you want to use this model checkout the following notenbook: sagemaker/18_inferentia_inference
TAGS
#transformers #roberta #text-classification #generated_from_trainer #dataset-clinc_oos #model-index #autotrain_compatible #endpoints_compatible #region-us 
 # Neuron conversation # MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-distilled-clinc

This model is a fine-tuned version of nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.9389999 ## Deploy/use Model

If you want to use this model checkout the following notenbook: sagemaker/18_inferentia_inference
passage: TAGS
#transformers #roberta #text-classification #generated_from_trainer #dataset-clinc_oos #model-index #autotrain_compatible #endpoints_compatible #region-us 
# Neuron conversation# MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-distilled-clinc

This model is a fine-tuned version of nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.9389999## Deploy/use Model

If you want to use this model checkout the following notenbook: sagemaker/18_inferentia_inference