Instructions to use avkumararun/bert-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use avkumararun/bert-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="avkumararun/bert-tiny")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("avkumararun/bert-tiny") model = AutoModelForSequenceClassification.from_pretrained("avkumararun/bert-tiny") - Notebooks
- Google Colab
- Kaggle
Upload BertForSequenceClassification
Browse files- config.json +20 -10
- model.safetensors +2 -2
config.json
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"hidden_dropout_prob": 0.1,
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"hidden_size": 128,
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"id2label": {
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"initializer_range": 0.02,
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"intermediate_size": 512,
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"is_decoder": false,
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"label2id": {
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},
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"hidden_dropout_prob": 0.1,
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"hidden_size": 128,
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"id2label": {
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"0": "QUERY",
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"1": "ASSERTION",
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"2": "DIRECTIVE",
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"3": "SALUTATION",
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"4": "VALIDATION",
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"5": "REJECTION",
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"6": "CHALLENGE",
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"7": "STALL",
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"8": "COURTESY",
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"9": "AMBIGUOUS"
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},
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"initializer_range": 0.02,
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"intermediate_size": 512,
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"is_decoder": false,
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"label2id": {
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"AMBIGUOUS": 9,
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"ASSERTION": 1,
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"CHALLENGE": 6,
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"COURTESY": 8,
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"DIRECTIVE": 2,
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"QUERY": 0,
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"REJECTION": 5,
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"SALUTATION": 3,
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"STALL": 7,
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"VALIDATION": 4
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},
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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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
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version https://git-lfs.github.com/spec/v1
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oid sha256:99a22ad15e2aac815888b3624994f9bee7701aa80b026461371c14e7292415e9
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size 17553432
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