Text Classification
Transformers
PyTorch
TensorBoard
English
distilbert
sms
spam
detection
text-embeddings-inference
Instructions to use akuysal/English-SMS-classification-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use akuysal/English-SMS-classification-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="akuysal/English-SMS-classification-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("akuysal/English-SMS-classification-model") model = AutoModelForSequenceClassification.from_pretrained("akuysal/English-SMS-classification-model") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 1
Browse files
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.
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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": "4.27.1",
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"vocab_size": 30522
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}
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pytorch_model.bin
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runs/Mar20_07-20-05_e4cd0c04e20a/1679296875.625083/events.out.tfevents.1679296875.e4cd0c04e20a.1103.1
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runs/Mar20_07-20-05_e4cd0c04e20a/events.out.tfevents.1679296875.e4cd0c04e20a.1103.0
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size 4184
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tokenizer_config.json
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"do_lower_case": true,
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"mask_token": "[MASK]",
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"model_max_length": 512,
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"name_or_path": "distilbert-base-uncased",
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"special_tokens_map_file": null,
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"do_lower_case": true,
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"mask_token": "[MASK]",
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"model_max_length": 512,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"special_tokens_map_file": null,
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training_args.bin
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