Text Classification
Transformers
TensorBoard
Safetensors
English
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hartunka/bert_base_rand_100_v2_wnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/bert_base_rand_100_v2_wnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/bert_base_rand_100_v2_wnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hartunka/bert_base_rand_100_v2_wnli") model = AutoModelForSequenceClassification.from_pretrained("Hartunka/bert_base_rand_100_v2_wnli") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 356392a2da3ff235608921960088df601af6b1917a33f98fa2284a80e0235778
- Size of remote file:
- 5.37 kB
- SHA256:
- 27287baf5b27132fe74365ac3377d510ea01c7145a2db9651cad6cec8acc36e2
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