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")# pip install -U transformers accelerate # 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
Download training_run.json from caeher/mbert-sv: direct link, hf CLI and curl.
- Browser
- Download file 2.6 kB
-
https://huggingface.co/caeher/mbert-sv/resolve/main/training_run.json
- Command line
-
hf download hf://caeher/mbert-sv/training_run.json
-
curl -L -o training_run.json https://huggingface.co/caeher/mbert-sv/resolve/main/training_run.json
2.6 kB
| { | |
| "recorded_at": "2026-11-03T04:53:59.758584+00:00", | |
| "git_commit": "13116ddf90b379ac21bdf6499c1b8e775b4c349a", | |
| "base_model": "bert-base-multilingual-cased", | |
| "model_dir": "C:\\Users\\user6921\\Desktop\\cde-ml\\ml-metricas\\versiones\\v6\\model", | |
| "seed": 42, | |
| "checkpoint_identity": { | |
| "checkpoint": "checkpoint-4050", | |
| "epoch": 5.0, | |
| "global_step": 4050, | |
| "best_metric_val_f1_macro": 0.8156693593379709, | |
| "best_model_checkpoint": "checkpoint-4050" | |
| }, | |
| "parameters": { | |
| "total_params": 177856516, | |
| "trainable_params": 177856516 | |
| }, | |
| "training_arguments": { | |
| "output_dir": "versiones\\v6\\model", | |
| "num_train_epochs": 5, | |
| "max_steps": -1, | |
| "per_device_train_batch_size": 16, | |
| "per_device_eval_batch_size": 32, | |
| "learning_rate": 2e-05, | |
| "warmup_ratio": 0.1, | |
| "weight_decay": 0.01, | |
| "seed": 42, | |
| "fp16": true, | |
| "eval_strategy": "epoch", | |
| "save_strategy": "epoch", | |
| "load_best_model_at_end": true, | |
| "metric_for_best_model": "f1_macro" | |
| }, | |
| "runtime": { | |
| "global_step": 4050, | |
| "epoch": 3.0, | |
| "best_metric": 0.8156693593379709, | |
| "best_model_checkpoint": "versiones\\v6\\model\\checkpoint-4050" | |
| }, | |
| "dataset": { | |
| "dataset_metadata_path": "data\\processed\\metadata.json", | |
| "dataset_version": "1.9.0", | |
| "train_total": 21585, | |
| "train.csv_sha256": "7ab0ad17422624b674bc12e06cbbb1b796c1ed389d0e4aa872efcd0d3a2111d4", | |
| "val.csv_sha256": "7967228844f8715a40e24668a2eec9b99137d36f53b303715e2dbf07cf0de383", | |
| "test.csv_sha256": "b63a04906bedda1befb41a7d147c582dc0ebee2f02f0cb3617ab461bdf932294" | |
| }, | |
| "training_recipe": { | |
| "release": "v6", | |
| "synthetic_policy": "b2_full", | |
| "synthetic_effective_fraction": null, | |
| "threat_synthetic_effective_n": 90.0, | |
| "platform_class_boost": "none", | |
| "label_smoothing": 0.0, | |
| "loss_type": "ce", | |
| "ordinal_weight": 0.3, | |
| "focal_gamma": 2.0, | |
| "layerwise_lr_decay": null, | |
| "classifier_dropout": null, | |
| "learning_rate": null, | |
| "epochs": 5, | |
| "early_stopping_patience": null, | |
| "seed": 42, | |
| "use_b2": false, | |
| "exclude_adversarial_overlap": false, | |
| "use_v1_class_weights": false, | |
| "init_model_dir": "versiones\\v4\\model", | |
| "train_rows": 21585, | |
| "val_rows": 459 | |
| }, | |
| "eval_metrics": { | |
| "eval_loss": 1.155531883239746, | |
| "eval_f1_macro": 0.817364486135725, | |
| "eval_accuracy": 0.8235294117647058, | |
| "eval_runtime": 1.2714, | |
| "eval_samples_per_second": 361.022, | |
| "eval_steps_per_second": 11.798, | |
| "epoch": 5.0 | |
| } | |
| } | |