Instructions to use relbert/relbert-albert-base-nce-semeval2012 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use relbert/relbert-albert-base-nce-semeval2012 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="relbert/relbert-albert-base-nce-semeval2012")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("relbert/relbert-albert-base-nce-semeval2012") model = AutoModel.from_pretrained("relbert/relbert-albert-base-nce-semeval2012", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download classification.json from relbert/relbert-albert-base-nce-semeval2012: direct link, hf CLI and curl.
- Browser
- Download file 4 kB
-
https://huggingface.co/relbert/relbert-albert-base-nce-semeval2012/resolve/main/classification.json
- Command line
-
hf download hf://relbert/relbert-albert-base-nce-semeval2012/classification.json
-
curl -L -o classification.json https://huggingface.co/relbert/relbert-albert-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": 0.880066295012807, "test/f1_macro": 0.8753757463611488, "test/f1_micro": 0.880066295012807, "test/p_macro": 0.876043997695696, "test/p_micro": 0.880066295012807, "test/r_macro": 0.8759001248744429, "test/r_micro": 0.880066295012807}, "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": 0.7826291079812207, "test/f1_macro": 0.5287375767546086, "test/f1_micro": 0.7826291079812207, "test/p_macro": 0.5717605933164622, "test/p_micro": 0.7826291079812207, "test/r_macro": 0.49831316208113235, "test/r_micro": 0.7826291079812207}, "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": 0.5812567713976164, "test/f1_macro": 0.5671693927108489, "test/f1_micro": 0.5812567713976164, "test/p_macro": 0.5751199285365679, "test/p_micro": 0.5812567713976164, "test/r_macro": 0.5641312242948139, "test/r_micro": 0.5812567713976164}, "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": 0.9288446824789595, "test/f1_macro": 0.8200653410286133, "test/f1_micro": 0.9288446824789595, "test/p_macro": 0.8181871213728776, "test/p_micro": 0.9288446824789595, "test/r_macro": 0.8224571100779642, "test/r_micro": 0.9288446824789595}, "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": 0.8558445628329677, "test/f1_macro": 0.8539588731534683, "test/f1_micro": 0.8558445628329677, "test/p_macro": 0.8498820709195044, "test/p_micro": 0.8558445628329677, "test/r_macro": 0.8585656855950652, "test/r_micro": 0.8558445628329677}} |