Instructions to use joseph10/tiny-bert-sst2-distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use joseph10/tiny-bert-sst2-distilled with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="joseph10/tiny-bert-sst2-distilled")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("joseph10/tiny-bert-sst2-distilled") model = AutoModelForSequenceClassification.from_pretrained("joseph10/tiny-bert-sst2-distilled") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 1
Browse files- logs/events.out.tfevents.1701811770.joseph_legion.26688.12 +2 -2
- logs/events.out.tfevents.1701811929.joseph_legion.26688.13 +3 -0
- logs/events.out.tfevents.1701812006.joseph_legion.26688.14 +3 -0
- logs/events.out.tfevents.1701812087.joseph_legion.26688.15 +3 -0
- model.safetensors +1 -1
- run-14/checkpoint-120/config.json +38 -0
- run-14/checkpoint-120/model.safetensors +3 -0
- run-14/checkpoint-120/optimizer.pt +3 -0
- run-14/checkpoint-120/rng_state.pth +3 -0
- run-14/checkpoint-120/scheduler.pt +3 -0
- run-14/checkpoint-120/special_tokens_map.json +7 -0
- run-14/checkpoint-120/tokenizer.json +0 -0
- run-14/checkpoint-120/tokenizer_config.json +57 -0
- run-14/checkpoint-120/trainer_state.json +39 -0
- run-14/checkpoint-120/training_args.bin +3 -0
- run-14/checkpoint-120/vocab.txt +0 -0
- training_args.bin +1 -1
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run-14/checkpoint-120/config.json
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run-14/checkpoint-120/special_tokens_map.json
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run-14/checkpoint-120/tokenizer.json
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run-14/checkpoint-120/tokenizer_config.json
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run-14/checkpoint-120/trainer_state.json
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{
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}
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],
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"logging_steps": 500,
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| 28 |
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}
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run-14/checkpoint-120/training_args.bin
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run-14/checkpoint-120/vocab.txt
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training_args.bin
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