Text Classification
Transformers
PyTorch
Turkish
bert
deprem-clf-v1
Eval Results (legacy)
text-embeddings-inference
Instructions to use deprem-ml/multilabel_earthquake_tweet_intent_bert_base_turkish_cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deprem-ml/multilabel_earthquake_tweet_intent_bert_base_turkish_cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="deprem-ml/multilabel_earthquake_tweet_intent_bert_base_turkish_cased")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("deprem-ml/multilabel_earthquake_tweet_intent_bert_base_turkish_cased") model = AutoModelForSequenceClassification.from_pretrained("deprem-ml/multilabel_earthquake_tweet_intent_bert_base_turkish_cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b3145189213cce653183f7fdf34e31031b88926674477dcf8de1cfa1f49102b6
- Size of remote file:
- 443 MB
- SHA256:
- 1581c622dfa7c3e98bcb82a245e2eba7cba77e73134a7ff16a2f3ede2fbe41d7
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