Instructions to use Synthyra/ESMplusplus_large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Synthyra/ESMplusplus_large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Synthyra/ESMplusplus_large", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("Synthyra/ESMplusplus_large", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload modeling_esm_plusplus.py with huggingface_hub
Browse files- modeling_esm_plusplus.py +1 -0
modeling_esm_plusplus.py
CHANGED
|
@@ -950,6 +950,7 @@ class ESMplusplusForSequenceClassification(ESMplusplusForMaskedLM, EmbeddingMixi
|
|
| 950 |
output_attentions: Optional[bool] = None,
|
| 951 |
output_hidden_states: Optional[bool] = None,
|
| 952 |
return_dict: Optional[bool] = None, # to play nice with HF adjacent packages
|
|
|
|
| 953 |
) -> ESMplusplusOutput:
|
| 954 |
"""Forward pass for sequence classification.
|
| 955 |
|
|
|
|
| 950 |
output_attentions: Optional[bool] = None,
|
| 951 |
output_hidden_states: Optional[bool] = None,
|
| 952 |
return_dict: Optional[bool] = None, # to play nice with HF adjacent packages
|
| 953 |
+
**kwargs,
|
| 954 |
) -> ESMplusplusOutput:
|
| 955 |
"""Forward pass for sequence classification.
|
| 956 |
|