Text Classification
Transformers
PyTorch
Turkish
bert
deprem-clf-v13
Eval Results (legacy)
text-embeddings-inference
Instructions to use deprem-ml/deprem_bert_128k_v13_beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deprem-ml/deprem_bert_128k_v13_beta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="deprem-ml/deprem_bert_128k_v13_beta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("deprem-ml/deprem_bert_128k_v13_beta") model = AutoModelForSequenceClassification.from_pretrained("deprem-ml/deprem_bert_128k_v13_beta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from deprem-ml/deprem_bert_128k_v13_beta: direct link, hf CLI and curl.
- Browser
- Download file 737 MB
-
https://huggingface.co/deprem-ml/deprem_bert_128k_v13_beta/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://deprem-ml/deprem_bert_128k_v13_beta/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/deprem-ml/deprem_bert_128k_v13_beta/resolve/main/pytorch_model.bin
737 MB
- Xet hash:
- 38c6b17174a098d89448a7c23d8f7f279514ce408f54e3bdad880a02fb9d040f
- Size of remote file:
- 737 MB
- SHA256:
- 0cd471f76d2248215dddc2d39ffa6789c4c7e91ad50bad91e909d500737d038e
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