Instructions to use Jsevisal/balanced-augmented-roberta-gest-pred-seqeval-partialmatch-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jsevisal/balanced-augmented-roberta-gest-pred-seqeval-partialmatch-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Jsevisal/balanced-augmented-roberta-gest-pred-seqeval-partialmatch-2")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Jsevisal/balanced-augmented-roberta-gest-pred-seqeval-partialmatch-2") model = AutoModelForTokenClassification.from_pretrained("Jsevisal/balanced-augmented-roberta-gest-pred-seqeval-partialmatch-2") - 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:aeffa66d5054f3662a276144134e0ab6621523c6f9d6a47431fc6bec75264932
|
| 3 |
+
size 496377496
|