Instructions to use deepanshu30699/distilbert-sst2-mlops with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepanshu30699/distilbert-sst2-mlops with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="deepanshu30699/distilbert-sst2-mlops")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("deepanshu30699/distilbert-sst2-mlops") model = AutoModelForSequenceClassification.from_pretrained("deepanshu30699/distilbert-sst2-mlops", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload DistilBertForSequenceClassification
Browse files- config.json +3 -6
- model.safetensors +1 -1
config.json
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{
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"activation": "gelu",
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"architectures": [
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"DistilBertForSequenceClassification"
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],
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"attention_dropout": 0.1,
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"bos_token_id": null,
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"dim": 768,
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"dropout": 0.1,
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"dtype": "float32",
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"eos_token_id": null,
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"hidden_dim": 3072,
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"id2label": {
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"0": "negative",
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"seq_classif_dropout": 0.2,
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"sinusoidal_pos_embds": false,
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"tie_weights_": true,
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"
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"transformers_version": "
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"use_cache": false,
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"vocab_size": 30522
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}
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{
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"_name_or_path": "distilbert-base-uncased",
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"activation": "gelu",
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"architectures": [
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"DistilBertForSequenceClassification"
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],
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"attention_dropout": 0.1,
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"dim": 768,
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"dropout": 0.1,
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"hidden_dim": 3072,
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"id2label": {
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"0": "negative",
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"seq_classif_dropout": 0.2,
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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": "4.44.2",
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"vocab_size": 30522
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 267832560
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version https://git-lfs.github.com/spec/v1
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oid sha256:ccdf6da4c6d5dd25ca283161354f3734b64f2bf1854a857ba9b43c08cfb5aaf0
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size 267832560
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