End of training
Browse files
README.md
ADDED
|
@@ -0,0 +1,78 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
library_name: transformers
|
| 3 |
+
license: cc-by-4.0
|
| 4 |
+
base_model: l3cube-pune/indic-sentence-bert-nli
|
| 5 |
+
tags:
|
| 6 |
+
- generated_from_trainer
|
| 7 |
+
metrics:
|
| 8 |
+
- accuracy
|
| 9 |
+
- precision
|
| 10 |
+
- recall
|
| 11 |
+
- f1
|
| 12 |
+
model-index:
|
| 13 |
+
- name: indic-sentence-bert-nli-roman-urdu-fine-grained
|
| 14 |
+
results: []
|
| 15 |
+
---
|
| 16 |
+
|
| 17 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
| 18 |
+
should probably proofread and complete it, then remove this comment. -->
|
| 19 |
+
|
| 20 |
+
# indic-sentence-bert-nli-roman-urdu-fine-grained
|
| 21 |
+
|
| 22 |
+
This model is a fine-tuned version of [l3cube-pune/indic-sentence-bert-nli](https://huggingface.co/l3cube-pune/indic-sentence-bert-nli) on the None dataset.
|
| 23 |
+
It achieves the following results on the evaluation set:
|
| 24 |
+
- Loss: 0.7424
|
| 25 |
+
- Accuracy: 0.7858
|
| 26 |
+
- Precision: 0.7111
|
| 27 |
+
- Recall: 0.6798
|
| 28 |
+
- F1: 0.6906
|
| 29 |
+
|
| 30 |
+
## Model description
|
| 31 |
+
|
| 32 |
+
More information needed
|
| 33 |
+
|
| 34 |
+
## Intended uses & limitations
|
| 35 |
+
|
| 36 |
+
More information needed
|
| 37 |
+
|
| 38 |
+
## Training and evaluation data
|
| 39 |
+
|
| 40 |
+
More information needed
|
| 41 |
+
|
| 42 |
+
## Training procedure
|
| 43 |
+
|
| 44 |
+
### Training hyperparameters
|
| 45 |
+
|
| 46 |
+
The following hyperparameters were used during training:
|
| 47 |
+
- learning_rate: 5e-05
|
| 48 |
+
- train_batch_size: 32
|
| 49 |
+
- eval_batch_size: 128
|
| 50 |
+
- seed: 42
|
| 51 |
+
- gradient_accumulation_steps: 2
|
| 52 |
+
- total_train_batch_size: 64
|
| 53 |
+
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
| 54 |
+
- lr_scheduler_type: linear
|
| 55 |
+
- num_epochs: 10
|
| 56 |
+
|
| 57 |
+
### Training results
|
| 58 |
+
|
| 59 |
+
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|
| 60 |
+
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
|
| 61 |
+
| 1.1702 | 1.0 | 113 | 1.1398 | 0.5901 | 0.3936 | 0.3331 | 0.2566 |
|
| 62 |
+
| 0.9227 | 2.0 | 226 | 0.8477 | 0.7001 | 0.2670 | 0.3508 | 0.2990 |
|
| 63 |
+
| 0.8216 | 3.0 | 339 | 0.7744 | 0.7309 | 0.3829 | 0.4267 | 0.3918 |
|
| 64 |
+
| 0.6698 | 4.0 | 452 | 0.6684 | 0.7713 | 0.5727 | 0.5493 | 0.5269 |
|
| 65 |
+
| 0.6337 | 5.0 | 565 | 0.5499 | 0.8340 | 0.6059 | 0.6291 | 0.6115 |
|
| 66 |
+
| 0.5396 | 6.0 | 678 | 0.4947 | 0.8428 | 0.6067 | 0.6571 | 0.6247 |
|
| 67 |
+
| 0.469 | 7.0 | 791 | 0.4368 | 0.8756 | 0.7950 | 0.7254 | 0.7261 |
|
| 68 |
+
| 0.4571 | 8.0 | 904 | 0.3816 | 0.9105 | 0.8661 | 0.8083 | 0.8305 |
|
| 69 |
+
| 0.4099 | 9.0 | 1017 | 0.3544 | 0.9237 | 0.8699 | 0.8494 | 0.8558 |
|
| 70 |
+
| 0.3605 | 10.0 | 1130 | 0.3385 | 0.9256 | 0.8819 | 0.8436 | 0.8576 |
|
| 71 |
+
|
| 72 |
+
|
| 73 |
+
### Framework versions
|
| 74 |
+
|
| 75 |
+
- Transformers 4.45.1
|
| 76 |
+
- Pytorch 2.4.0
|
| 77 |
+
- Datasets 3.0.1
|
| 78 |
+
- Tokenizers 0.20.0
|