Instructions to use mircoboettcher/bert-ecom-ner-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mircoboettcher/bert-ecom-ner-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mircoboettcher/bert-ecom-ner-test")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mircoboettcher/bert-ecom-ner-test") model = AutoModelForTokenClassification.from_pretrained("mircoboettcher/bert-ecom-ner-test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload BertForTokenClassification
Browse files- config.json +92 -0
- model.safetensors +3 -0
config.json
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{
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"_name_or_path": "bert-base-uncased",
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"architectures": [
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"BertForTokenClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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"0": "O",
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"1": "B-purpose",
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"2": "I-purpose",
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"3": "B-display",
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"4": "I-display",
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"5": "B-brand",
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"6": "I-brand",
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"7": "B-storage",
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"8": "I-storage",
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"9": "B-design",
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"10": "I-design",
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"11": "B-performance",
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"12": "I-performance",
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"13": "B-model",
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"14": "I-model",
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"15": "B-price",
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"16": "I-price",
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"17": "B-weight",
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"18": "I-weight",
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"19": "B-ram",
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"20": "I-ram",
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"21": "B-processor",
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"22": "I-processor",
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"23": "B-size",
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"24": "I-size",
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"25": "B-os",
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"26": "I-os",
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"27": "B-software",
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"28": "I-software",
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"29": "B-battery",
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"30": "I-battery"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"B-battery": 29,
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"B-brand": 5,
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"B-design": 9,
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"B-display": 3,
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"B-model": 13,
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"B-os": 25,
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"B-performance": 11,
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"B-price": 15,
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"B-processor": 21,
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"B-purpose": 1,
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"B-ram": 19,
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"B-size": 23,
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"B-software": 27,
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"B-storage": 7,
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"B-weight": 17,
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"I-battery": 30,
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"I-brand": 6,
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"I-design": 10,
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"I-display": 4,
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"I-model": 14,
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"I-os": 26,
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"I-performance": 12,
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"I-price": 16,
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"I-processor": 22,
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"I-purpose": 2,
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"I-ram": 20,
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"I-size": 24,
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"I-software": 28,
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"I-storage": 8,
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"I-weight": 18,
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"O": 0
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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_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.47.1",
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"type_vocab_size": 2,
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"use_cache": true,
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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:e9d13a090fe36021d65608e7080664c69c8bfbfc32821b0b3b9895bb83f67e28
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size 435685292
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