Instructions to use hf-internal-testing/tiny-random-XLMForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-XLMForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-internal-testing/tiny-random-XLMForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-XLMForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-internal-testing/tiny-random-XLMForTokenClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b93ef8430bbdec3243de84ca2abfcc1d9f921f8d2002c77a1e0f9d60d8e0ab5c
- Size of remote file:
- 4.21 MB
- SHA256:
- f2c12292b12c8cf1bc8a5af50887026c69922c8c88e462877761c55ab669fb88
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