Instructions to use dima806/chatgpt-tweets-engagement with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/chatgpt-tweets-engagement with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dima806/chatgpt-tweets-engagement")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dima806/chatgpt-tweets-engagement") model = AutoModelForSequenceClassification.from_pretrained("dima806/chatgpt-tweets-engagement", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload . with huggingface_hub
Browse files- checkpoint-11276/config.json +34 -0
- checkpoint-11276/optimizer.pt +3 -0
- checkpoint-11276/pytorch_model.bin +3 -0
- checkpoint-11276/rng_state.pth +3 -0
- checkpoint-11276/scheduler.pt +3 -0
- checkpoint-11276/trainer_state.json +0 -0
- checkpoint-11276/training_args.bin +3 -0
- checkpoint-22552/config.json +34 -0
- checkpoint-22552/optimizer.pt +3 -0
- checkpoint-22552/pytorch_model.bin +3 -0
- checkpoint-22552/rng_state.pth +3 -0
- checkpoint-22552/scheduler.pt +3 -0
- checkpoint-22552/trainer_state.json +0 -0
- checkpoint-22552/training_args.bin +3 -0
- pytorch_model.bin +1 -1
- training_args.bin +1 -1
checkpoint-11276/config.json
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{
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"_name_or_path": "vinai/bertweet-base",
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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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"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": "NOT ENGAGED",
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"1": "ENGAGED"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 130,
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"model_type": "roberta",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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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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"tokenizer_class": "BertweetTokenizer",
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"torch_dtype": "float32",
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"transformers_version": "4.28.1",
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 64001
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}
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checkpoint-11276/optimizer.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:682b7a4d257c886b1dedf7fc07e4ee952da85c3318b75d6b24b5ef52105f7fb0
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size 1079329925
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checkpoint-11276/pytorch_model.bin
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size 539676341
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checkpoint-11276/rng_state.pth
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oid sha256:36f2d2ce9a8623db7a68d32ebc12b58ec732fd257a728ad1e68165451b08e476
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size 14575
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checkpoint-11276/scheduler.pt
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checkpoint-11276/trainer_state.json
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checkpoint-11276/training_args.bin
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checkpoint-22552/config.json
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{
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"_name_or_path": "vinai/bertweet-base",
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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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"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": "NOT ENGAGED",
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"1": "ENGAGED"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 130,
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"model_type": "roberta",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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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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"tokenizer_class": "BertweetTokenizer",
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"torch_dtype": "float32",
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"transformers_version": "4.28.1",
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 64001
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}
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checkpoint-22552/optimizer.pt
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size 1079329925
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checkpoint-22552/pytorch_model.bin
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size 539676341
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checkpoint-22552/rng_state.pth
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size 14575
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checkpoint-22552/scheduler.pt
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size 627
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checkpoint-22552/trainer_state.json
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checkpoint-22552/training_args.bin
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pytorch_model.bin
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training_args.bin
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size 3515
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size 3515
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