Instructions to use relbert/relbert-roberta-base-nce-semeval2012-average with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use relbert/relbert-roberta-base-nce-semeval2012-average with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="relbert/relbert-roberta-base-nce-semeval2012-average")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("relbert/relbert-roberta-base-nce-semeval2012-average") model = AutoModel.from_pretrained("relbert/relbert-roberta-base-nce-semeval2012-average", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download classification.json from relbert/relbert-roberta-base-nce-semeval2012-average: direct link, hf CLI and curl.
- Browser
- Download file 4.01 kB
-
https://huggingface.co/relbert/relbert-roberta-base-nce-semeval2012-average/resolve/refs%2Fpr%2F1/classification.json
- Command line
-
hf download hf://relbert/relbert-roberta-base-nce-semeval2012-average@refs/pr/1/classification.json
-
curl -L -o classification.json https://huggingface.co/relbert/relbert-roberta-base-nce-semeval2012-average/resolve/refs%2Fpr%2F1/classification.json
4.01 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": 0.8998041283712521, "test/f1_macro": 0.8977934264238211, "test/f1_micro": 0.8998041283712521, "test/p_macro": 0.8917486357523923, "test/p_micro": 0.8998041283712521, "test/r_macro": 0.9051892726118357, "test/r_micro": 0.8998041283712521}, "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": 0.8272300469483568, "test/f1_macro": 0.6397137935143544, "test/f1_micro": 0.8272300469483568, "test/p_macro": 0.6619784132799931, "test/p_micro": 0.8272300469483568, "test/r_macro": 0.6213250025985675, "test/r_micro": 0.8272300469483568}, "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": 0.6462621885157096, "test/f1_macro": 0.6446245083980608, "test/f1_micro": 0.6462621885157096, "test/p_macro": 0.6537160185135298, "test/p_micro": 0.6462621885157096, "test/r_macro": 0.6384690628989079, "test/r_micro": 0.6462621885157096}, "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": 0.9412951241566391, "test/f1_macro": 0.8510085443796092, "test/f1_micro": 0.9412951241566391, "test/p_macro": 0.867195617415925, "test/p_micro": 0.9412951241566391, "test/r_macro": 0.8368361305440628, "test/r_micro": 0.9412951241566391}, "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": 0.8752742087120025, "test/f1_macro": 0.8731443734393283, "test/f1_micro": 0.8752742087120025, "test/p_macro": 0.8682973841822011, "test/p_micro": 0.8752742087120025, "test/r_macro": 0.8787091659586669, "test/r_micro": 0.8752742087120025}} |