Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use BaxterAI/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BaxterAI/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BaxterAI/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BaxterAI/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("BaxterAI/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 1
Browse files- config.json +17 -20
- pytorch_model.bin +2 -2
- runs/May23_09-38-41_2d362c5ccea5/1653298727.100855/events.out.tfevents.1653298727.2d362c5ccea5.75.1 +3 -0
- runs/May23_09-38-41_2d362c5ccea5/events.out.tfevents.1653298727.2d362c5ccea5.75.0 +3 -0
- special_tokens_map.json +1 -1
- tokenizer.json +0 -0
- tokenizer_config.json +1 -1
- training_args.bin +1 -1
config.json
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{
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"_name_or_path": "
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"architectures": [
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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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"initializer_range": 0.02,
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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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"torch_dtype": "float32",
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"transformers_version": "4.19.2",
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"use_cache": true,
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"vocab_size": 50265
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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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"initializer_range": 0.02,
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"max_position_embeddings": 512,
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"model_type": "distilbert",
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"n_heads": 12,
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"n_layers": 6,
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"pad_token_id": 0,
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"problem_type": "single_label_classification",
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"qa_dropout": 0.1,
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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": "4.19.2",
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"vocab_size": 30522
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}
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pytorch_model.bin
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runs/May23_09-38-41_2d362c5ccea5/1653298727.100855/events.out.tfevents.1653298727.2d362c5ccea5.75.1
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runs/May23_09-38-41_2d362c5ccea5/events.out.tfevents.1653298727.2d362c5ccea5.75.0
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special_tokens_map.json
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{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}
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tokenizer.json
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tokenizer_config.json
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{"
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{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "num_labels": 2, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "distilbert-base-uncased", "tokenizer_class": "DistilBertTokenizer"}
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training_args.bin
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size 3247
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