Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
bert
Generated from Trainer
nlu
intent-classification
Eval Results (legacy)
text-embeddings-inference
Instructions to use cartesinus/multilingual_minilm-amazon-massive-intent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cartesinus/multilingual_minilm-amazon-massive-intent with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cartesinus/multilingual_minilm-amazon-massive-intent")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cartesinus/multilingual_minilm-amazon-massive-intent") model = AutoModelForSequenceClassification.from_pretrained("cartesinus/multilingual_minilm-amazon-massive-intent") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): 47dd557
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README.md
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license: mit
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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- f1
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model-index:
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- name: multilingual_minilm-amazon-massive-intent
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results:
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datasets:
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- AmazonScience/massive
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language:
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# multilingual_minilm-amazon-massive-intent
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This model is a fine-tuned version of [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) on the
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It achieves the following results on the evaluation set:
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- Loss: 0.8941
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- Accuracy: 0.8234
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license: mit
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tags:
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- generated_from_trainer
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- nlu
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- intent-classification
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metrics:
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- accuracy
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- f1
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model-index:
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- name: multilingual_minilm-amazon-massive-intent
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results:
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- task:
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name: intent-classification
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type: intent-classification
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dataset:
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name: MASSIVE
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type: AmazonScience/massive
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split: test
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metrics:
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- name: F1
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type: f1
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value: 0.8234
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datasets:
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- AmazonScience/massive
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language:
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# multilingual_minilm-amazon-massive-intent
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This model is a fine-tuned version of [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) on the [MASSIVE1.1](https://huggingface.co/datasets/AmazonScience/massive) dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.8941
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- Accuracy: 0.8234
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