README.md

---
language:
- bo
library_name: transformers
pipeline_tag: token-classification
tags:
- tibetan
- token-classification
- layer-detection
base_model: jhu-clsp/mmBERT-base
datasets:
- karma689/layer_detection
---

# mmbert_base_v1.2

Tibetan annotation-layer token classifier fine-tuned from
[`jhu-clsp/mmBERT-base`](https://huggingface.co/jhu-clsp/mmBERT-base).

## Validation metrics

The values below are from the **best** evaluation record.
The complete evaluation history and original Trainer state are included in
`training/metrics.json` and `training/trainer_state.json`.

| Metric | Value |
|---|---:|
| `epoch` | `8.0` |
| `eval_f1` | `0.078113428181641` |
| `eval_f1_author` | `0.6329113924050632` |
| `eval_f1_chapter` | `0.5358490566037736` |
| `eval_f1_quote` | `0.022925944252020453` |
| `eval_f1_sabche` | `0.19551934826883913` |
| `eval_f1_tsawa` | `0.03828125` |
| `eval_f1_yigchung` | `0.02306079664570231` |
| `eval_loss` | `0.15565519034862518` |
| `eval_precision` | `0.052703555649063746` |
| `eval_recall` | `0.15083546590395905` |
| `eval_runtime` | `165.0116` |
| `eval_samples_per_second` | `8.581` |
| `eval_soft_f1_tol0` | `0.078113428181641` |
| `eval_soft_f1_tol1` | `0.09284739816799845` |
| `eval_soft_precision_tol0` | `0.052703555649063746` |
| `eval_soft_precision_tol1` | `0.06264464548706081` |
| `eval_soft_recall_tol0` | `0.15083546590395905` |
| `eval_soft_recall_tol1` | `0.17928646695769984` |
| `eval_steps_per_second` | `4.291` |
| `step` | `10832` |

## Training parameters

Every value from the experiment's `config.yaml` is listed below. The original
file is also included as `training/config.yaml`.

| Parameter | Value |
|---|---|
| `experiment_name` | `mmbert_base_v1.2` |
| `dataset_repo` | `karma689/layer_detection` |
| `dataset_revision` | `null` |
| `scope_policy` | `confirmed` |
| `local_fallback.repos_dir` | `../../Data/repos` |
| `local_fallback.split_csv` | `../../Data/audit/split.csv` |
| `checkpoint` | `jhu-clsp/mmBERT-base` |
| `fallback_checkpoint` | `KoichiYasuoka/bert-base-tibetan` |
| `attn_implementation` | `flash_attention_2` |
| `attn_fallback_implementation` | `sdpa` |
| `max_length` | `8192` |
| `stride` | `5120` |
| `pad_to_multiple_of` | `8` |
| `window_cache_dir` | `window_cache` |
| `tokenize_num_workers` | `8` |
| `masked_layers` | `["BookTitle"]` |
| `learning_rate` | `1e-05` |
| `train_batch_size` | `2` |
| `eval_batch_size` | `2` |
| `gradient_accumulation_steps` | `4` |
| `num_train_epochs` | `10` |
| `weight_decay` | `0.01` |
| `warmup_ratio` | `0.2` |
| `max_grad_norm` | `0.3` |
| `class_weighting` | `True` |
| `class_weight_cap` | `5.0` |
| `o_class_weight` | `0.07` |
| `fp16` | `False` |
| `bf16` | `True` |
| `gradient_checkpointing` | `False` |
| `dataloader_num_workers` | `4` |
| `dataloader_pin_memory` | `True` |
| `tf32` | `True` |
| `seed` | `42` |
| `logging_steps` | `20` |
| `save_total_limit` | `3` |
| `resume_from_checkpoint` | `null` |
| `eval_strategy` | `epoch` |
| `save_strategy` | `epoch` |
| `load_best_model_at_end` | `True` |
| `metric_for_best_model` | `soft_f1_tol1` |
| `greater_is_better` | `True` |
| `early_stopping_patience` | `3` |
| `report_to` | `["wandb"]` |
| `wandb.project` | `layer-detection` |
| `wandb.mode` | `online` |
| `output_dir` | `outputs` |

## Labels

| ID | BIO label |
|---:|---|
| 0 | `O` |
| 1 | `B-QUOTE` |
| 2 | `I-QUOTE` |
| 3 | `B-SABCHE` |
| 4 | `I-SABCHE` |
| 5 | `B-TSAWA` |
| 6 | `I-TSAWA` |
| 7 | `B-YIGCHUNG` |
| 8 | `I-YIGCHUNG` |
| 9 | `B-CHAPTER` |
| 10 | `I-CHAPTER` |
| 11 | `B-AUTHOR` |
| 12 | `I-AUTHOR` |
| 13 | `B-BOOKTITLE` |
| 14 | `I-BOOKTITLE` |

## Usage

```python
from transformers import AutoModelForTokenClassification, AutoTokenizer

model_id = "karma689/mmbert-base-layer-detection-v1.2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForTokenClassification.from_pretrained(model_id)
```

Long texts should use the same window settings shown in the training parameters.