Text Classification
Transformers
ONNX
Safetensors
English
stratabert
diagnostic
long-context
custom-code
custom_code
Instructions to use dplotnikov/stratabert-tiny-ag-news-smoke with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dplotnikov/stratabert-tiny-ag-news-smoke with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dplotnikov/stratabert-tiny-ag-news-smoke", trust_remote_code=True)# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("dplotnikov/stratabert-tiny-ag-news-smoke", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| """Mask helpers for padding-safe model code.""" | |
| from __future__ import annotations | |
| import torch | |
| def make_attention_mask(input_ids: torch.Tensor, attention_mask: torch.Tensor | None, pad_token_id: int) -> torch.Tensor: | |
| if attention_mask is None: | |
| return input_ids.ne(pad_token_id) | |
| return attention_mask.to(dtype=torch.bool, device=input_ids.device) | |
| def masked_hidden(hidden_states: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor: | |
| return hidden_states * attention_mask.to(hidden_states.dtype).unsqueeze(-1) | |
| def reverse_valid(x: torch.Tensor, attention_mask: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: | |
| return torch.flip(x, dims=[1]), torch.flip(attention_mask, dims=[1]) | |
| def segment_reset_mask(segment_ids: torch.Tensor | None, attention_mask: torch.Tensor) -> torch.Tensor | None: | |
| if segment_ids is None: | |
| return None | |
| reset = torch.zeros_like(attention_mask, dtype=torch.bool) | |
| reset[:, 0] = True | |
| reset[:, 1:] = segment_ids[:, 1:] != segment_ids[:, :-1] | |
| return reset & attention_mask | |