Instructions to use sraj/Merge_Drop_MARK_FastText with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sraj/Merge_Drop_MARK_FastText with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="sraj/Merge_Drop_MARK_FastText")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("sraj/Merge_Drop_MARK_FastText") model = AutoModelForMaskedLM.from_pretrained("sraj/Merge_Drop_MARK_FastText", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d92576523ffead2de4ba6911e0a418d19aa4a62bd7089535080503daa900ea10
- Size of remote file:
- 299 MB
- SHA256:
- f9eae11593e3f5aa3469989d6e97545c42ed2632fc6a7b7afe871c73aade8866
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