Instructions to use AlexKoff88/bert_nm_mnli_sparse_quantized_90 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AlexKoff88/bert_nm_mnli_sparse_quantized_90 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AlexKoff88/bert_nm_mnli_sparse_quantized_90")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AlexKoff88/bert_nm_mnli_sparse_quantized_90") model = AutoModelForSequenceClassification.from_pretrained("AlexKoff88/bert_nm_mnli_sparse_quantized_90") - Notebooks
- Google Colab
- Kaggle
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license: apache-2.0
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# Quantized BERT-base model with 90% of
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The pruned and quantized model in the OpenVINO IR. The pruned model was taken from this source and quantized with the code below using HF Optimum for OpenVINO:
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```python
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from functools import partial
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license: apache-2.0
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# Quantized BERT-base MNLI model with 90% of usntructured sparsity
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The pruned and quantized model in the OpenVINO IR. The pruned model was taken from this [source](https://huggingface.co/neuralmagic/oBERT-12-downstream-pruned-unstructured-90-mnli) and quantized with the code below using HF Optimum for OpenVINO:
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```python
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from functools import partial
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