Text Classification
Transformers
Safetensors
Laya
English
system-one
calibrated-decisions
logical-fallacy
debate
modernbert
Instructions to use BryanSnappCTO/laya-fallacies with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BryanSnappCTO/laya-fallacies with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BryanSnappCTO/laya-fallacies")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("BryanSnappCTO/laya-fallacies", device_map="auto") - Laya
How to use BryanSnappCTO/laya-fallacies with Laya:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Download train_report.json from BryanSnappCTO/laya-fallacies: direct link, hf CLI and curl.
- Browser
- Download file 1.96 kB
-
https://huggingface.co/BryanSnappCTO/laya-fallacies/resolve/main/train_report.json
- Command line
-
hf download hf://BryanSnappCTO/laya-fallacies/train_report.json
-
curl -L -o train_report.json https://huggingface.co/BryanSnappCTO/laya-fallacies/resolve/main/train_report.json
1.96 kB
| { | |
| "args": { | |
| "task_file": "scripts/fallacies.json", | |
| "dataset": null, | |
| "dataset_revision": null, | |
| "split": "train", | |
| "text_column": null, | |
| "label_column": null, | |
| "model_dir": null, | |
| "base_revision": "1c5edc17a7acd8701df6fc341c0d179f1c62c982", | |
| "output_dir": "./tmp/stage1", | |
| "device": "auto", | |
| "amp": false, | |
| "epochs": 4, | |
| "micro_batch": 8, | |
| "grad_accum": 4, | |
| "group_size": 4, | |
| "lr_encoder": 2.5e-05, | |
| "lr_head": 0.0001, | |
| "weight_decay": 0.01, | |
| "sigma_start": 0.4, | |
| "sigma_end": 0.1, | |
| "seed": 42, | |
| "max_items": 0, | |
| "calib_max": 400, | |
| "val_frac": 0.1, | |
| "negative_file": "/Users/brian/git/receptron/laya/scripts/negatives.jsonl", | |
| "extra_file": "none", | |
| "dry_run": false, | |
| "dry_steps": 5, | |
| "finalize_only": false | |
| }, | |
| "base_model": "convaiinnovations/laya", | |
| "base_revision": "1c5edc17a7acd8701df6fc341c0d179f1c62c982", | |
| "base_model_dir": "/Users/brian/.cache/huggingface/hub/models--convaiinnovations--laya/snapshots/1c5edc17a7acd8701df6fc341c0d179f1c62c982", | |
| "dataset": "tasksource/logical-fallacy", | |
| "dataset_revision": "37e9b0537a86e72e9eaf6ee8c9a27d872a944103", | |
| "env": { | |
| "laya": "0.3.5", | |
| "torch": "2.14.0", | |
| "transformers": "5.17.0", | |
| "datasets": "5.0.1", | |
| "safetensors": "0.8.0", | |
| "numpy": "2.5.3" | |
| }, | |
| "epochs": [ | |
| { | |
| "epoch": 1, | |
| "avg_loss": 1.9571953387693926 | |
| }, | |
| { | |
| "epoch": 2, | |
| "avg_loss": 1.4149250892075624 | |
| }, | |
| { | |
| "epoch": 3, | |
| "avg_loss": 0.9954640326174823 | |
| }, | |
| { | |
| "epoch": 4, | |
| "avg_loss": 0.7398663367737424 | |
| } | |
| ], | |
| "fitted_temperatures": [ | |
| 1.8769, | |
| 1.2, | |
| 1.2 | |
| ], | |
| "val": { | |
| "accuracy": 0.654320987654321, | |
| "mean_argmax_confidence": 0.7798909438858307, | |
| "n": 243 | |
| }, | |
| "test": { | |
| "accuracy": 0.5048923679060665, | |
| "mean_argmax_confidence": 0.6494024801860817, | |
| "n": 511 | |
| }, | |
| "train_items": 2196, | |
| "device": "mps", | |
| "amp": false | |
| } |