Feature Extraction
Transformers
PyTorch
TensorFlow
JAX
multilingual
Portuguese
bert
bert-base-multilingual-cased
semantic role labeling
finetuned
Instructions to use liaad/srl-pt_mbert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use liaad/srl-pt_mbert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="liaad/srl-pt_mbert-base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("liaad/srl-pt_mbert-base") model = AutoModel.from_pretrained("liaad/srl-pt_mbert-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
dfd2706
1
Parent(s): fc97bbd
upload flax model
Browse files- flax_model.msgpack +3 -0
flax_model.msgpack
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:bb795bd6187b7967e49eea6f8d87fd5cfbbc8ead1517fbf42ff4fe4d1caeb57f
|
| 3 |
+
size 711420911
|