Text Classification
Transformers
Safetensors
Spanish
bert
hate-speech
toxicity
spanish
el-salvador
mbert
Eval Results (legacy)
text-embeddings-inference
Instructions to use caeher/mbert-sv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use caeher/mbert-sv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="caeher/mbert-sv")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("caeher/mbert-sv") model = AutoModelForSequenceClassification.from_pretrained("caeher/mbert-sv", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "accuracy": 0.782608695652174, | |
| "balanced_accuracy": 0.7757451391051162, | |
| "f1_macro": 0.7777194765714894, | |
| "f1_weighted": 0.7828842207289413, | |
| "precision_macro": 0.7808732555624674, | |
| "recall_macro": 0.7757451391051162, | |
| "ordinal_mae": 0.3239130434782609, | |
| "ordinal_rmse": 0.7561343335907864, | |
| "large_error_pct": 8.91304347826087, | |
| "qwk": 0.7361556982343499, | |
| "kappa_linear": 0.7182345879992764, | |
| "mcc": 0.7035845189708845, | |
| "precision_No Tóxico": 0.8495575221238938, | |
| "recall_No Tóxico": 0.7804878048780488, | |
| "f1_No Tóxico": 0.8135593220338984, | |
| "support_No Tóxico": 123, | |
| "precision_Lenguaje Ofensivo": 0.7738095238095238, | |
| "recall_Lenguaje Ofensivo": 0.8176100628930818, | |
| "f1_Lenguaje Ofensivo": 0.7951070336391437, | |
| "support_Lenguaje Ofensivo": 159, | |
| "precision_Discurso de Odio": 0.7346938775510204, | |
| "recall_Discurso de Odio": 0.7578947368421053, | |
| "f1_Discurso de Odio": 0.7461139896373057, | |
| "support_Discurso de Odio": 95, | |
| "precision_Amenazas/Violencia": 0.7654320987654321, | |
| "recall_Amenazas/Violencia": 0.7469879518072289, | |
| "f1_Amenazas/Violencia": 0.7560975609756098, | |
| "support_Amenazas/Violencia": 83, | |
| "roc_auc_ovr_macro": 0.9221629865233141, | |
| "log_loss": 0.8721387005957749, | |
| "ece": 0.15694192051887512, | |
| "brier_macro": 0.09395594522356987, | |
| "classification_report": " precision recall f1-score support\n\n No Tóxico 0.85 0.78 0.81 123\n Lenguaje Ofensivo 0.77 0.82 0.80 159\n Discurso de Odio 0.73 0.76 0.75 95\nAmenazas/Violencia 0.77 0.75 0.76 83\n\n accuracy 0.78 460\n macro avg 0.78 0.78 0.78 460\n weighted avg 0.78 0.78 0.78 460\n", | |
| "f1_bootstrap_mean": 0.7775980516278476, | |
| "f1_bootstrap_lo": 0.7357670523307825, | |
| "f1_bootstrap_hi": 0.8143980633933287 | |
| } | |