Instructions to use research-backup/relbert-roberta-base-nce-b-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-b-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-b-nell")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("research-backup/relbert-roberta-base-nce-b-nell") model = AutoModel.from_pretrained("research-backup/relbert-roberta-base-nce-b-nell", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download classification.json from research-backup/relbert-roberta-base-nce-b-nell: direct link, hf CLI and curl.
- Browser
- Download file 4.01 kB
-
https://huggingface.co/research-backup/relbert-roberta-base-nce-b-nell/resolve/main/classification.json
- Command line
-
hf download hf://research-backup/relbert-roberta-base-nce-b-nell/classification.json
-
curl -L -o classification.json https://huggingface.co/research-backup/relbert-roberta-base-nce-b-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": 0.8912159108030737, "test/f1_macro": 0.882832586768981, "test/f1_micro": 0.8912159108030737, "test/p_macro": 0.8731668877628546, "test/p_micro": 0.8912159108030737, "test/r_macro": 0.893276809884822, "test/r_micro": 0.8912159108030737}, "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": 0.7922535211267606, "test/f1_macro": 0.5399502573058529, "test/f1_micro": 0.7922535211267606, "test/p_macro": 0.5699066481464536, "test/p_micro": 0.7922535211267606, "test/r_macro": 0.520765684046437, "test/r_micro": 0.7922535211267606}, "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": 0.6045503791982665, "test/f1_macro": 0.6027670935592911, "test/f1_micro": 0.6045503791982665, "test/p_macro": 0.6249371647309738, "test/p_micro": 0.6045503791982665, "test/r_macro": 0.5882112187507004, "test/r_micro": 0.6045503791982665}, "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": 0.9501286777491827, "test/f1_macro": 0.8672983510521417, "test/f1_micro": 0.9501286777491827, "test/p_macro": 0.8722942488317735, "test/p_micro": 0.9501286777491827, "test/r_macro": 0.8626275344219145, "test/r_micro": 0.9501286777491827}, "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": 0.8749608273268568, "test/f1_macro": 0.8713638706052124, "test/f1_micro": 0.8749608273268568, "test/p_macro": 0.8718694455079358, "test/p_micro": 0.8749608273268568, "test/r_macro": 0.8709418061253836, "test/r_micro": 0.8749608273268568}} |