Text Classification
Transformers
TensorBoard
Safetensors
English
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hartunka/bert_base_rand_20_v2_mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/bert_base_rand_20_v2_mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/bert_base_rand_20_v2_mnli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hartunka/bert_base_rand_20_v2_mnli") model = AutoModelForSequenceClassification.from_pretrained("Hartunka/bert_base_rand_20_v2_mnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Hartunka/bert_base_rand_20_v2_mnli: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/Hartunka/bert_base_rand_20_v2_mnli/resolve/main/training_args.bin
- Command line
-
hf download hf://Hartunka/bert_base_rand_20_v2_mnli/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Hartunka/bert_base_rand_20_v2_mnli/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- e05b4ab1eb6331f52958c3e9e51201c74bbe093869e5da53a12aaf809ad1f7a0
- Size of remote file:
- 5.37 kB
- SHA256:
- 4910e94e747dce67d064a4a9d32d13ea76b784393b43efee604a4dc93af913ab
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