Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
roberta
text-embeddings-inference
Instructions to use smeintadmin/image_intents with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use smeintadmin/image_intents with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="smeintadmin/image_intents")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("smeintadmin/image_intents") model = AutoModelForSequenceClassification.from_pretrained("smeintadmin/image_intents", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
e1f66c5
1
Parent(s): 0cfa69a
Upload 9 files
Browse files- config.json +14 -13
- merges.txt +0 -0
- pytorch_model.bin +2 -2
- special_tokens_map.json +13 -5
- tokenizer.json +0 -0
- tokenizer_config.json +11 -9
- training_args.bin +1 -1
- vocab.json +0 -0
config.json
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{
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"_name_or_path": "
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"architectures": [
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size":
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"initializer_range": 0.02,
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"intermediate_size":
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"layer_norm_eps": 1e-
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"max_position_embeddings":
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"model_type": "
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"num_attention_heads":
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"num_hidden_layers":
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"pad_token_id":
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"position_embedding_type": "absolute",
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"problem_type": "single_label_classification",
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"torch_dtype": "float32",
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"transformers_version": "4.31.0",
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"type_vocab_size":
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"use_cache": true,
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"vocab_size":
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}
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{
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"_name_or_path": "roberta-large",
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"architectures": [
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"RobertaForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": 0,
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"classifier_dropout": null,
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"eos_token_id": 2,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 1024,
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 514,
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"model_type": "roberta",
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"num_attention_heads": 16,
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"num_hidden_layers": 24,
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"problem_type": "single_label_classification",
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"torch_dtype": "float32",
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"transformers_version": "4.31.0",
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 50265
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}
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merges.txt
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pytorch_model.bin
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size 1421582769
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special_tokens_map.json
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"bos_token": "<s>",
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"cls_token": "<s>",
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"eos_token": "</s>",
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"mask_token": {
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"content": "<mask>",
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"lstrip": true,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": "<pad>",
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"sep_token": "</s>",
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"unk_token": "<unk>"
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tokenizer.json
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tokenizer_config.json
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"clean_up_tokenization_spaces": true,
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"model_max_length": 512,
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"unk_token": "[UNK]"
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"add_prefix_space": false,
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"bos_token": "<s>",
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"clean_up_tokenization_spaces": true,
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"cls_token": "<s>",
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"eos_token": "</s>",
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"errors": "replace",
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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": "</s>",
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"tokenizer_class": "RobertaTokenizer",
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"trim_offsets": true,
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"unk_token": "<unk>"
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}
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training_args.bin
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size 3899
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size 3899
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vocab.json
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