Instructions to use relbert/relbert-bert-base-nce-semeval2012 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use relbert/relbert-bert-base-nce-semeval2012 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="relbert/relbert-bert-base-nce-semeval2012")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("relbert/relbert-bert-base-nce-semeval2012") model = AutoModel.from_pretrained("relbert/relbert-bert-base-nce-semeval2012", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download classification.json from relbert/relbert-bert-base-nce-semeval2012: direct link, hf CLI and curl.
- Browser
- Download file 4 kB
-
https://huggingface.co/relbert/relbert-bert-base-nce-semeval2012/resolve/main/classification.json
- Command line
-
hf download hf://relbert/relbert-bert-base-nce-semeval2012/classification.json
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curl -L -o classification.json https://huggingface.co/relbert/relbert-bert-base-nce-semeval2012/resolve/main/classification.json
4 kB
| {"lexical_relation_classification/BLESS": {"classifier_config": {"activation": "relu", "alpha": 0.0001, "batch_size": "auto", "beta_1": 0.9, "beta_2": 0.999, "early_stopping": false, "epsilon": 1e-08, "hidden_layer_sizes": [100], "learning_rate": "constant", "learning_rate_init": 0.001, "max_fun": 15000, "max_iter": 200, "momentum": 0.9, "n_iter_no_change": 10, "nesterovs_momentum": true, "power_t": 0.5, "random_state": 0, "shuffle": true, "solver": "adam", "tol": 0.0001, "validation_fraction": 0.1, "verbose": false, "warm_start": false}, "test/accuracy": 0.908693686906735, "test/f1_macro": 0.9055400457466346, "test/f1_micro": 0.908693686906735, "test/p_macro": 0.9099863768301774, "test/p_micro": 0.908693686906735, "test/r_macro": 0.9014547960168372, "test/r_micro": 0.908693686906735}, "lexical_relation_classification/CogALexV": {"classifier_config": {"activation": "relu", "alpha": 0.0001, "batch_size": "auto", "beta_1": 0.9, "beta_2": 0.999, "early_stopping": false, "epsilon": 1e-08, "hidden_layer_sizes": [100], "learning_rate": "constant", "learning_rate_init": 0.001, "max_fun": 15000, "max_iter": 200, "momentum": 0.9, "n_iter_no_change": 10, "nesterovs_momentum": true, "power_t": 0.5, "random_state": 0, "shuffle": true, "solver": "adam", "tol": 0.0001, "validation_fraction": 0.1, "verbose": false, "warm_start": false}, "test/accuracy": 0.8070422535211268, "test/f1_macro": 0.5665007891204441, "test/f1_micro": 0.8070422535211269, "test/p_macro": 0.604069659132816, "test/p_micro": 0.8070422535211268, "test/r_macro": 0.5379500342039009, "test/r_micro": 0.8070422535211268}, "lexical_relation_classification/EVALution": {"classifier_config": {"activation": "relu", "alpha": 0.0001, "batch_size": "auto", "beta_1": 0.9, "beta_2": 0.999, "early_stopping": false, "epsilon": 1e-08, "hidden_layer_sizes": [100], "learning_rate": "constant", "learning_rate_init": 0.001, "max_fun": 15000, "max_iter": 200, "momentum": 0.9, "n_iter_no_change": 10, "nesterovs_momentum": true, "power_t": 0.5, "random_state": 0, "shuffle": true, "solver": "adam", "tol": 0.0001, "validation_fraction": 0.1, "verbose": false, "warm_start": false}, "test/accuracy": 0.6180931744312026, "test/f1_macro": 0.6141134603634173, "test/f1_micro": 0.6180931744312026, "test/p_macro": 0.6280590473797103, "test/p_micro": 0.6180931744312026, "test/r_macro": 0.6053997740960108, "test/r_micro": 0.6180931744312026}, "lexical_relation_classification/K&H+N": {"classifier_config": {"activation": "relu", "alpha": 0.0001, "batch_size": "auto", "beta_1": 0.9, "beta_2": 0.999, "early_stopping": false, "epsilon": 1e-08, "hidden_layer_sizes": [100], "learning_rate": "constant", "learning_rate_init": 0.001, "max_fun": 15000, "max_iter": 200, "momentum": 0.9, "n_iter_no_change": 10, "nesterovs_momentum": true, "power_t": 0.5, "random_state": 0, "shuffle": true, "solver": "adam", "tol": 0.0001, "validation_fraction": 0.1, "verbose": false, "warm_start": false}, "test/accuracy": 0.955484454336788, "test/f1_macro": 0.8772168996387368, "test/f1_micro": 0.955484454336788, "test/p_macro": 0.9146552911671193, "test/p_micro": 0.955484454336788, "test/r_macro": 0.8510643791276826, "test/r_micro": 0.955484454336788}, "lexical_relation_classification/ROOT09": {"classifier_config": {"activation": "relu", "alpha": 0.0001, "batch_size": "auto", "beta_1": 0.9, "beta_2": 0.999, "early_stopping": false, "epsilon": 1e-08, "hidden_layer_sizes": [100], "learning_rate": "constant", "learning_rate_init": 0.001, "max_fun": 15000, "max_iter": 200, "momentum": 0.9, "n_iter_no_change": 10, "nesterovs_momentum": true, "power_t": 0.5, "random_state": 0, "shuffle": true, "solver": "adam", "tol": 0.0001, "validation_fraction": 0.1, "verbose": false, "warm_start": false}, "test/accuracy": 0.8874960827326857, "test/f1_macro": 0.8854532712455304, "test/f1_micro": 0.8874960827326857, "test/p_macro": 0.8824800075945705, "test/p_micro": 0.8874960827326857, "test/r_macro": 0.8887571942583555, "test/r_micro": 0.8874960827326857}} |