Instructions to use research-backup/relbert-roberta-base-nce-a-nell with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use research-backup/relbert-roberta-base-nce-a-nell with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="research-backup/relbert-roberta-base-nce-a-nell")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("research-backup/relbert-roberta-base-nce-a-nell") model = AutoModel.from_pretrained("research-backup/relbert-roberta-base-nce-a-nell", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download classification.json from research-backup/relbert-roberta-base-nce-a-nell: direct link, hf CLI and curl.
- Browser
- Download file 4.01 kB
-
https://huggingface.co/research-backup/relbert-roberta-base-nce-a-nell/resolve/main/classification.json
- Command line
-
hf download hf://research-backup/relbert-roberta-base-nce-a-nell/classification.json
-
curl -L -o classification.json https://huggingface.co/research-backup/relbert-roberta-base-nce-a-nell/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": 0.8987494349856863, "test/f1_macro": 0.890050406980326, "test/f1_micro": 0.8987494349856864, "test/p_macro": 0.8875116515535861, "test/p_micro": 0.8987494349856863, "test/r_macro": 0.8938613731964725, "test/r_micro": 0.8987494349856863}, "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": 0.8011737089201878, "test/f1_macro": 0.5692353291338583, "test/f1_micro": 0.8011737089201879, "test/p_macro": 0.5940718935233757, "test/p_micro": 0.8011737089201878, "test/r_macro": 0.5503942431001262, "test/r_micro": 0.8011737089201878}, "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": 0.6105092091007583, "test/f1_macro": 0.5995431523479036, "test/f1_micro": 0.6105092091007583, "test/p_macro": 0.6286715552108619, "test/p_micro": 0.6105092091007583, "test/r_macro": 0.5829130332128429, "test/r_micro": 0.6105092091007583}, "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": 0.9476942338457258, "test/f1_macro": 0.856425065439868, "test/f1_micro": 0.9476942338457258, "test/p_macro": 0.8544397340559369, "test/p_micro": 0.9476942338457258, "test/r_macro": 0.8586886722866884, "test/r_micro": 0.9476942338457258}, "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": 0.8824819805703541, "test/f1_macro": 0.8824104543652189, "test/f1_micro": 0.8824819805703541, "test/p_macro": 0.8759737931202952, "test/p_micro": 0.8824819805703541, "test/r_macro": 0.8905194302279558, "test/r_micro": 0.8824819805703541}} |