Feature Extraction
Transformers
Safetensors
English
flow_gru
sequence-classification
sandbox
event-sequences
gru
custom_code
Eval Results (legacy)
Instructions to use harpertoken/flow with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harpertoken/flow with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="harpertoken/flow", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("harpertoken/flow", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 448 Bytes
7d6d550 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | {
"architectures": [
"FlowGRU"
],
"auto_map": {
"AutoConfig": "modeling_flow.FlowConfig",
"AutoModel": "modeling_flow.FlowGRU"
},
"dropout": 0.25,
"dtype": "float32",
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1",
"2": "LABEL_2",
"3": "LABEL_3"
},
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1,
"LABEL_2": 2,
"LABEL_3": 3
},
"model_type": "flow_gru",
"transformers_version": "5.19.0"
}
|