Instructions to use ashercn97/ashbert-v001 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ashercn97/ashbert-v001 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ashercn97/ashbert-v001", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("ashercn97/ashbert-v001", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update model.py
Browse files
model.py
CHANGED
|
@@ -29,7 +29,7 @@ from transformers.modeling_outputs import (
|
|
| 29 |
SequenceClassifierOutput,
|
| 30 |
)
|
| 31 |
|
| 32 |
-
from rotary import precompute_freqs_cis, apply_rotary_emb
|
| 33 |
import torch.nn.functional as F
|
| 34 |
|
| 35 |
|
|
|
|
| 29 |
SequenceClassifierOutput,
|
| 30 |
)
|
| 31 |
|
| 32 |
+
from .rotary import precompute_freqs_cis, apply_rotary_emb
|
| 33 |
import torch.nn.functional as F
|
| 34 |
|
| 35 |
|