Instructions to use Gowthamvemula/Telugu_Sentimental_Analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gowthamvemula/Telugu_Sentimental_Analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Gowthamvemula/Telugu_Sentimental_Analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Gowthamvemula/Telugu_Sentimental_Analysis") model = AutoModelForSequenceClassification.from_pretrained("Gowthamvemula/Telugu_Sentimental_Analysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload BertForSequenceClassification
Browse files- config.json +2 -1
- model.safetensors +1 -1
config.json
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{
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"architectures": [
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"BertForSequenceClassification"
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],
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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.
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 119547
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{
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"_name_or_path": "/content/telugu_bert_model_new",
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"architectures": [
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"BertForSequenceClassification"
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],
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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": "4.48.3",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 119547
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 711446532
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version https://git-lfs.github.com/spec/v1
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oid sha256:6e72b0f77f13682d69c57ce23637915d9e8a2cfda1f5174c346339485ca6765f
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size 711446532
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