Instructions to use mohammadmahdinouri/mol-qqp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mohammadmahdinouri/mol-qqp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mohammadmahdinouri/mol-qqp")# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("mohammadmahdinouri/mol-qqp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from mohammadmahdinouri/mol-qqp: direct link, hf CLI and curl.
- Browser
- Download file 915 Bytes
-
https://huggingface.co/mohammadmahdinouri/mol-qqp/resolve/main/config.json
- Command line
-
hf download hf://mohammadmahdinouri/mol-qqp/config.json
-
curl -L -o config.json https://huggingface.co/mohammadmahdinouri/mol-qqp/resolve/main/config.json
915 Bytes
| { | |
| "adapter_reduction": 16, | |
| "architectures": [ | |
| "ModernAlbertForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "dtype": "float32", | |
| "embedding_size": 128, | |
| "expert_intermediate_size": 4096, | |
| "group_depth": 4, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 1024, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 2624, | |
| "layer_norm_eps": 1e-06, | |
| "load_balancing_loss_coef": 0.2, | |
| "lora_alpha": 32, | |
| "lora_rank": 16, | |
| "max_position_embeddings": 8192, | |
| "model_type": "ModernALBERT", | |
| "num_attention_heads": 16, | |
| "num_expert_modules": 4, | |
| "num_experts": 8, | |
| "num_hidden_layers": 24, | |
| "pad_token_id": 0, | |
| "problem_type": "single_label_classification", | |
| "router_jitter_noise": 0.01, | |
| "top_k": 2, | |
| "transformers_version": "4.56.1", | |
| "use_adapter": true, | |
| "use_cache": true, | |
| "use_moa": true, | |
| "vocab_size": 50368 | |
| } | |