Instructions to use tner/roberta-base-tweetner7-2021 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tner/roberta-base-tweetner7-2021 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="tner/roberta-base-tweetner7-2021")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("tner/roberta-base-tweetner7-2021") model = AutoModelForTokenClassification.from_pretrained("tner/roberta-base-tweetner7-2021", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Adding `safetensors` variant of this model
Verified signatureThis is an automated PR created with https://huggingface.co/spaces/safetensors/convert This new file is equivalent to `pytorch_model.bin` but safe in the sense that no arbitrary code can be put into it. These files also happen to load much faster than their pytorch counterpart: https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb The widgets on your model page will run using this model even if this is not merged making sure the file actually works. If you find any issues: please report here: https://huggingface.co/spaces/safetensors/convert/discussions Feel free to ignore this PR.1c355f1
-
model update
13cb0e9
-
model update
b719a8a -
model update
7593efc -
model update
e864408
-
model update
bb5afe9 -
add tokenizer
555bac7 -
add model
c01e7ab
-
model update
bff5d44 -
add tokenizer
5f62ce9 -
add model
e05517d -
initial commit
b6530a2