Instructions to use kt220/review_classification_bert_base_jp_v3_ratio1_1_add_dropout-epoch30_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kt220/review_classification_bert_base_jp_v3_ratio1_1_add_dropout-epoch30_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kt220/review_classification_bert_base_jp_v3_ratio1_1_add_dropout-epoch30_v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kt220/review_classification_bert_base_jp_v3_ratio1_1_add_dropout-epoch30_v2") model = AutoModelForSequenceClassification.from_pretrained("kt220/review_classification_bert_base_jp_v3_ratio1_1_add_dropout-epoch30_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 1
Browse files
config.json
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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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"intermediate_size": 3072,
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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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"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": "LABEL_0",
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"1": "LABEL_1",
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"2": "LABEL_2",
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"3": "LABEL_3"
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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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"LABEL_0": 0,
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"LABEL_1": 1,
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"LABEL_2": 2,
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"LABEL_3": 3
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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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model.safetensors
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size 444864520
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runs/Dec04_11-25-22_ec412c5b3ad2/events.out.tfevents.1701689132.ec412c5b3ad2.4683.0
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size 4856
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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size 4664
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