Instructions to use werent4/emo-gliclass-audio-bi-1-wds-0.015-red-sum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use werent4/emo-gliclass-audio-bi-1-wds-0.015-red-sum with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("werent4/emo-gliclass-audio-bi-1-wds-0.015-red-sum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload training_results.json with huggingface_hub
Browse files- training_results.json +46 -0
training_results.json
ADDED
|
@@ -0,0 +1,46 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"args": {
|
| 3 |
+
"model_name": null,
|
| 4 |
+
"encoder_model_name": "answerdotai/ModernBERT-base",
|
| 5 |
+
"audio_model_name": "facebook/wav2vec2-base-960h",
|
| 6 |
+
"save_path": "models/emo-gliclass-audio-bi-1-wds-0.015-red-sum",
|
| 7 |
+
"data_path": "datasets/processed_dataset.json",
|
| 8 |
+
"problem_type": "multi_label_classification",
|
| 9 |
+
"pooler_type": "first",
|
| 10 |
+
"scorer_type": "simple",
|
| 11 |
+
"architecture_type": "audio-bi-encoder",
|
| 12 |
+
"normalize_features": true,
|
| 13 |
+
"extract_text_features": false,
|
| 14 |
+
"prompt_first": true,
|
| 15 |
+
"use_lstm": false,
|
| 16 |
+
"squeeze_layers": false,
|
| 17 |
+
"shuffle_labels": true,
|
| 18 |
+
"num_epochs": 1,
|
| 19 |
+
"batch_size": 8,
|
| 20 |
+
"encoder_lr": 1e-05,
|
| 21 |
+
"others_lr": 1e-05,
|
| 22 |
+
"encoder_weight_decay": 0.015,
|
| 23 |
+
"others_weight_decay": 0.015,
|
| 24 |
+
"warmup_ratio": 0.05,
|
| 25 |
+
"lr_scheduler_type": "cosine",
|
| 26 |
+
"focal_loss_alpha": 0.9,
|
| 27 |
+
"focal_loss_gamma": 2.5,
|
| 28 |
+
"contrastive_loss_coef": 0.0,
|
| 29 |
+
"max_length": 1024,
|
| 30 |
+
"save_steps": 720,
|
| 31 |
+
"save_total_limit": 3,
|
| 32 |
+
"num_workers": 12,
|
| 33 |
+
"fp16": false
|
| 34 |
+
},
|
| 35 |
+
"eval_metrics": {
|
| 36 |
+
"eval_loss": 1.1119344234466553,
|
| 37 |
+
"eval_accuracy": 0.85234375,
|
| 38 |
+
"eval_precision": 0.8347429327943167,
|
| 39 |
+
"eval_recall": 0.85234375,
|
| 40 |
+
"eval_f1": 0.8413624188086402,
|
| 41 |
+
"eval_runtime": 15.7319,
|
| 42 |
+
"eval_samples_per_second": 81.363,
|
| 43 |
+
"eval_steps_per_second": 10.17,
|
| 44 |
+
"epoch": 1.0
|
| 45 |
+
}
|
| 46 |
+
}
|