Instructions to use dmargutierrez/bert-base-multilingual-cased-WNUT-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dmargutierrez/bert-base-multilingual-cased-WNUT-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="dmargutierrez/bert-base-multilingual-cased-WNUT-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("dmargutierrez/bert-base-multilingual-cased-WNUT-ner") model = AutoModelForTokenClassification.from_pretrained("dmargutierrez/bert-base-multilingual-cased-WNUT-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:99a36279edf7cb91254182ea9a9d723cd59171283ce684ff9657fd6a634a69cc
|
| 3 |
+
size 709118924
|