Instructions to use hf-internal-testing/tiny-random-Data2VecTextForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Data2VecTextForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-Data2VecTextForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-Data2VecTextForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-Data2VecTextForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e422ffb35b9a210aac0de4d88f90c5b28595c67fbbc55a4aec2c38b5f6206d7d
- Size of remote file:
- 471 kB
- SHA256:
- c1a5278e41ec4c90a384d257ad009fbab2f64d7d1d8c46bbd55ce225cbec1f2a
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