Instructions to use hendri/sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hendri/sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hendri/sentiment")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hendri/sentiment") model = AutoModel.from_pretrained("hendri/sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload config
Browse files- config.json +1 -3
config.json
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{
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"_name_or_path": "indobenchmark/indobert-large-p1",
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"_num_labels": 5,
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"architectures": [
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"
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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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"pooler_size_per_head": 128,
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"pooler_type": "first_token_transform",
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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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{
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"_num_labels": 5,
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"architectures": [
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"BertModel"
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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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"pooler_size_per_head": 128,
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"pooler_type": "first_token_transform",
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"position_embedding_type": "absolute",
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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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