Fill-Mask
Transformers
PyTorch
Safetensors
English
bert
splade
query-expansion
document-expansion
bag-of-words
passage-retrieval
knowledge-distillation
Instructions to use baseplate/splade-cocondenser-selfdistil with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use baseplate/splade-cocondenser-selfdistil with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="baseplate/splade-cocondenser-selfdistil")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("baseplate/splade-cocondenser-selfdistil") model = AutoModelForMaskedLM.from_pretrained("baseplate/splade-cocondenser-selfdistil", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update handler.py
Browse files- handler.py +6 -2
handler.py
CHANGED
|
@@ -9,7 +9,11 @@ class EndpointHandler():
|
|
| 9 |
model = AutoModelForMaskedLM.from_pretrained(path)
|
| 10 |
self.tokenizer = tokenizer
|
| 11 |
self.model = model
|
| 12 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
| 13 |
def __call__(self, data: Dict[str, Any]) -> List[Dict[Any, Any]]:
|
| 14 |
"""
|
| 15 |
data args:
|
|
@@ -20,7 +24,7 @@ class EndpointHandler():
|
|
| 20 |
"""
|
| 21 |
# get inputs
|
| 22 |
text = data.pop("text", data)
|
| 23 |
-
tokens = self.tokenizer(text, return_tensors='pt', padding=True)
|
| 24 |
outputs = self.model(**tokens)
|
| 25 |
results = []
|
| 26 |
for idx, x in enumerate(outputs.logits):
|
|
|
|
| 9 |
model = AutoModelForMaskedLM.from_pretrained(path)
|
| 10 |
self.tokenizer = tokenizer
|
| 11 |
self.model = model
|
| 12 |
+
if torch.cuda.is_available():
|
| 13 |
+
self.device = torch.device("cuda")
|
| 14 |
+
self.model.to(self.device)
|
| 15 |
+
else:
|
| 16 |
+
self.device = torch.device("cpu")
|
| 17 |
def __call__(self, data: Dict[str, Any]) -> List[Dict[Any, Any]]:
|
| 18 |
"""
|
| 19 |
data args:
|
|
|
|
| 24 |
"""
|
| 25 |
# get inputs
|
| 26 |
text = data.pop("text", data)
|
| 27 |
+
tokens = self.tokenizer(text, return_tensors='pt', padding=True).to(self.device)
|
| 28 |
outputs = self.model(**tokens)
|
| 29 |
results = []
|
| 30 |
for idx, x in enumerate(outputs.logits):
|