Feature Extraction
Transformers
Safetensors
roberta
mental-health
multi-label
mental-roberta-base
text-embeddings-inference
Instructions to use mahir2021/mental-roberta-bilstm-depemo2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mahir2021/mental-roberta-bilstm-depemo2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="mahir2021/mental-roberta-bilstm-depemo2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("mahir2021/mental-roberta-bilstm-depemo2") model = AutoModel.from_pretrained("mahir2021/mental-roberta-bilstm-depemo2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download hyperparameters.json from mahir2021/mental-roberta-bilstm-depemo2: direct link, hf CLI and curl.
- Browser
- Download file 208 Bytes
-
https://huggingface.co/mahir2021/mental-roberta-bilstm-depemo2/resolve/main/hyperparameters.json
- Command line
-
hf download hf://mahir2021/mental-roberta-bilstm-depemo2/hyperparameters.json
-
curl -L -o hyperparameters.json https://huggingface.co/mahir2021/mental-roberta-bilstm-depemo2/resolve/main/hyperparameters.json
208 Bytes
| { | |
| "seed": 42, | |
| "max_len": 192, | |
| "hidden_dim": 256, | |
| "batch_train": 16, | |
| "batch_eval": 32, | |
| "epochs": 5, | |
| "lr": 2e-05, | |
| "weight_decay": 0.01, | |
| "n_seeds": 2, | |
| "fm_weight": 0.1, | |
| "adv_weight": 0.05 | |
| } |