Commit
•
7a9b64d
1
Parent(s):
18e160c
update model card README.md
Browse files
README.md
ADDED
|
@@ -0,0 +1,84 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: cc-by-4.0
|
| 3 |
+
tags:
|
| 4 |
+
- generated_from_trainer
|
| 5 |
+
metrics:
|
| 6 |
+
- accuracy
|
| 7 |
+
- precision
|
| 8 |
+
- recall
|
| 9 |
+
- f1
|
| 10 |
+
model-index:
|
| 11 |
+
- name: hing-roberta-CM-run-5
|
| 12 |
+
results: []
|
| 13 |
+
---
|
| 14 |
+
|
| 15 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
| 16 |
+
should probably proofread and complete it, then remove this comment. -->
|
| 17 |
+
|
| 18 |
+
# hing-roberta-CM-run-5
|
| 19 |
+
|
| 20 |
+
This model is a fine-tuned version of [l3cube-pune/hing-roberta](https://huggingface.co/l3cube-pune/hing-roberta) on an unknown dataset.
|
| 21 |
+
It achieves the following results on the evaluation set:
|
| 22 |
+
- Loss: 2.6447
|
| 23 |
+
- Accuracy: 0.7525
|
| 24 |
+
- Precision: 0.7030
|
| 25 |
+
- Recall: 0.7120
|
| 26 |
+
- F1: 0.7064
|
| 27 |
+
|
| 28 |
+
## Model description
|
| 29 |
+
|
| 30 |
+
More information needed
|
| 31 |
+
|
| 32 |
+
## Intended uses & limitations
|
| 33 |
+
|
| 34 |
+
More information needed
|
| 35 |
+
|
| 36 |
+
## Training and evaluation data
|
| 37 |
+
|
| 38 |
+
More information needed
|
| 39 |
+
|
| 40 |
+
## Training procedure
|
| 41 |
+
|
| 42 |
+
### Training hyperparameters
|
| 43 |
+
|
| 44 |
+
The following hyperparameters were used during training:
|
| 45 |
+
- learning_rate: 3e-05
|
| 46 |
+
- train_batch_size: 8
|
| 47 |
+
- eval_batch_size: 8
|
| 48 |
+
- seed: 42
|
| 49 |
+
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
| 50 |
+
- lr_scheduler_type: linear
|
| 51 |
+
- num_epochs: 20
|
| 52 |
+
|
| 53 |
+
### Training results
|
| 54 |
+
|
| 55 |
+
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|
| 56 |
+
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
|
| 57 |
+
| 0.9492 | 1.0 | 497 | 0.7476 | 0.6157 | 0.6060 | 0.6070 | 0.5171 |
|
| 58 |
+
| 0.7013 | 2.0 | 994 | 0.7093 | 0.6982 | 0.6716 | 0.6864 | 0.6663 |
|
| 59 |
+
| 0.4871 | 3.0 | 1491 | 0.8294 | 0.7284 | 0.6714 | 0.6867 | 0.6723 |
|
| 60 |
+
| 0.3838 | 4.0 | 1988 | 1.1275 | 0.7505 | 0.6969 | 0.7025 | 0.6994 |
|
| 61 |
+
| 0.254 | 5.0 | 2485 | 1.3831 | 0.7264 | 0.6781 | 0.6975 | 0.6850 |
|
| 62 |
+
| 0.1765 | 6.0 | 2982 | 2.0625 | 0.7384 | 0.7068 | 0.6948 | 0.6896 |
|
| 63 |
+
| 0.1127 | 7.0 | 3479 | 1.9691 | 0.7425 | 0.6925 | 0.7065 | 0.6982 |
|
| 64 |
+
| 0.0757 | 8.0 | 3976 | 2.3871 | 0.7425 | 0.7183 | 0.6926 | 0.6924 |
|
| 65 |
+
| 0.0572 | 9.0 | 4473 | 2.4037 | 0.7344 | 0.6916 | 0.6929 | 0.6882 |
|
| 66 |
+
| 0.0458 | 10.0 | 4970 | 2.3062 | 0.7586 | 0.7174 | 0.7219 | 0.7164 |
|
| 67 |
+
| 0.0405 | 11.0 | 5467 | 2.5591 | 0.7445 | 0.6925 | 0.6964 | 0.6942 |
|
| 68 |
+
| 0.0292 | 12.0 | 5964 | 2.5215 | 0.7384 | 0.6875 | 0.6998 | 0.6917 |
|
| 69 |
+
| 0.0264 | 13.0 | 6461 | 2.7551 | 0.7586 | 0.7122 | 0.7035 | 0.7037 |
|
| 70 |
+
| 0.0299 | 14.0 | 6958 | 2.6536 | 0.7465 | 0.7114 | 0.7088 | 0.7035 |
|
| 71 |
+
| 0.0208 | 15.0 | 7455 | 2.5190 | 0.7505 | 0.6989 | 0.7083 | 0.7030 |
|
| 72 |
+
| 0.0263 | 16.0 | 7952 | 2.7092 | 0.7485 | 0.7076 | 0.6998 | 0.6962 |
|
| 73 |
+
| 0.0077 | 17.0 | 8449 | 2.5933 | 0.7525 | 0.7042 | 0.7143 | 0.7081 |
|
| 74 |
+
| 0.009 | 18.0 | 8946 | 2.5831 | 0.7485 | 0.6991 | 0.7152 | 0.7050 |
|
| 75 |
+
| 0.0108 | 19.0 | 9443 | 2.6360 | 0.7545 | 0.7050 | 0.7167 | 0.7098 |
|
| 76 |
+
| 0.0077 | 20.0 | 9940 | 2.6447 | 0.7525 | 0.7030 | 0.7120 | 0.7064 |
|
| 77 |
+
|
| 78 |
+
|
| 79 |
+
### Framework versions
|
| 80 |
+
|
| 81 |
+
- Transformers 4.20.1
|
| 82 |
+
- Pytorch 1.10.1+cu111
|
| 83 |
+
- Datasets 2.3.2
|
| 84 |
+
- Tokenizers 0.12.1
|