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
Download config.json from harpertoken/flow: direct link, hf CLI and curl.
- Browser
- Download file 448 Bytes
-
https://huggingface.co/harpertoken/flow/resolve/main/config.json
- Command line
-
hf download hf://harpertoken/flow/config.json
-
curl -L -o config.json https://huggingface.co/harpertoken/flow/resolve/main/config.json
448 Bytes
| { | |
| "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" | |
| } | |