Instructions to use canIjoin/datafun with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use canIjoin/datafun with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="canIjoin/datafun")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("canIjoin/datafun") model = AutoModelForTokenClassification.from_pretrained("canIjoin/datafun", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload pytorch_model.bin
Browse files- .gitattributes +1 -0
- pytorch_model.bin +3 -0
.gitattributes
CHANGED
|
@@ -1 +1,2 @@
|
|
| 1 |
flax_model.msgpack filter=lfs diff=lfs merge=lfs -text
|
|
|
|
|
|
| 1 |
flax_model.msgpack filter=lfs diff=lfs merge=lfs -text
|
| 2 |
+
pytorch_model.bin filter=lfs diff=lfs merge=lfs -text
|
pytorch_model.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:b865252516115c46bc508167fa2258f198bcce520eb7a1ac4fbf8d50dc361368
|
| 3 |
+
size 406892015
|