Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use JTH/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JTH/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JTH/results")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JTH/results") model = AutoModelForSequenceClassification.from_pretrained("JTH/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
update model card README.md
Browse files
README.md
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license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- onestop_english
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model-index:
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- name: results
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results: []
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# results
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id to label mapping:
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{LABEL_0: BASIC, LABEL_1: INTERMEDIATE, LABEL_2: ADVANCED}
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```
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the onestop_english dataset.
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## Model description
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### Framework versions
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- Transformers 4.21.
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- Pytorch 1.12.0+cu113
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- Datasets 2.4.0
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- Tokenizers 0.12.1
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license: apache-2.0
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tags:
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- generated_from_trainer
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model-index:
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- name: results
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results: []
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# results
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
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## Model description
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### Framework versions
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- Transformers 4.21.1
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- Pytorch 1.12.0+cu113
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- Datasets 2.4.0
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- Tokenizers 0.12.1
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