Instructions to use research-backup/relbert-roberta-large-nce-e-semeval2012 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use research-backup/relbert-roberta-large-nce-e-semeval2012 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="research-backup/relbert-roberta-large-nce-e-semeval2012")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("research-backup/relbert-roberta-large-nce-e-semeval2012") model = AutoModel.from_pretrained("research-backup/relbert-roberta-large-nce-e-semeval2012", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download classification.json from research-backup/relbert-roberta-large-nce-e-semeval2012: direct link, hf CLI and curl.
- Browser
- Download file 4.01 kB
-
https://huggingface.co/research-backup/relbert-roberta-large-nce-e-semeval2012/resolve/main/classification.json
- Command line
-
hf download hf://research-backup/relbert-roberta-large-nce-e-semeval2012/classification.json
-
curl -L -o classification.json https://huggingface.co/research-backup/relbert-roberta-large-nce-e-semeval2012/resolve/main/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": 0.9213500075335241, "test/f1_macro": 0.9170729678018987, "test/f1_micro": 0.9213500075335241, "test/p_macro": 0.9135189518365673, "test/p_micro": 0.9213500075335241, "test/r_macro": 0.9212177387658143, "test/r_micro": 0.9213500075335241}, "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": 0.8551643192488263, "test/f1_macro": 0.6925965667442509, "test/f1_micro": 0.8551643192488263, "test/p_macro": 0.7200507756798393, "test/p_micro": 0.8551643192488263, "test/r_macro": 0.6710169140522311, "test/r_micro": 0.8551643192488263}, "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": 0.6966413867822319, "test/f1_macro": 0.6791249033004563, "test/f1_micro": 0.6966413867822319, "test/p_macro": 0.6848713891450097, "test/p_micro": 0.6966413867822319, "test/r_macro": 0.6757594597070967, "test/r_micro": 0.6966413867822319}, "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": 0.9565973429783682, "test/f1_macro": 0.8699123751243014, "test/f1_micro": 0.9565973429783682, "test/p_macro": 0.8913173287320396, "test/p_micro": 0.9565973429783682, "test/r_macro": 0.8531003052992858, "test/r_micro": 0.9565973429783682}, "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": 0.8962707615167659, "test/f1_macro": 0.8935557085918555, "test/f1_micro": 0.8962707615167659, "test/p_macro": 0.8963813848040892, "test/p_micro": 0.8962707615167659, "test/r_macro": 0.8911779807661145, "test/r_micro": 0.8962707615167659}} |