Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use mtolgakbaba/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mtolgakbaba/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mtolgakbaba/results", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mtolgakbaba/results") model = AutoModelForSequenceClassification.from_pretrained("mtolgakbaba/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 56ec7f18967ab7b1050ac6d1bfacc7e2054af9efca26a2f2585c38a28935e9bf
- Size of remote file:
- 5.24 kB
- SHA256:
- e1a75d20759f71a5e77492b25fd9fb9cbce981e65e316000236e57eaae1893f9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.