Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use prakhars/instagram_caption_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prakhars/instagram_caption_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="prakhars/instagram_caption_classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("prakhars/instagram_caption_classifier") model = AutoModelForSequenceClassification.from_pretrained("prakhars/instagram_caption_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 500
Browse files- config.json +1 -1
- pytorch_model.bin +2 -2
- tokenizer.json +1 -6
config.json
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"sinusoidal_pos_embds": false,
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"tie_weights_": true,
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"torch_dtype": "float32",
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"transformers_version": "4.22.
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"vocab_size": 30522
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}
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"sinusoidal_pos_embds": false,
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"tie_weights_": true,
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"torch_dtype": "float32",
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"transformers_version": "4.22.2",
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"vocab_size": 30522
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:df76c9fd8ee786027baaed2756f00a938a839b032c2676e6a35f60122e3ca1d9
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size 267912753
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tokenizer.json
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{
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"version": "1.0",
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"truncation":
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"direction": "Right",
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"max_length": 512,
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"strategy": "LongestFirst",
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"stride": 0
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},
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"padding": null,
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"added_tokens": [
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"version": "1.0",
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"truncation": null,
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"padding": null,
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"added_tokens": [
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{
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