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XLM-Roberta-base-Finetuned-EN-AR-Parallel
iwan-rg/XLM-Roberta-base-Finetuned-EN-AR-Parallel
XLM-Roberta-base-Finetuned-EN-AR-Parallel is a machine learning model from iwan-rg. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- License
- mit
- Model type
- xlm-roberta
- Access
- Public
- Created
- Oct 28, 2024
- Updated
- Oct 28, 2024
- SHA
- 76cab873
Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
xlm-roberta-base-finetuned-Parallel-mlm-0.15-base-27OCT
This model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.9339
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 0.0998 | 100 | 1.4204 |
| No log | 0.1997 | 200 | 1.3432 |
| No log | 0.2995 | 300 | 1.3054 |
| No log | 0.3993 | 400 | 1.2756 |
| 1.5915 | 0.4992 | 500 | 1.2552 |
| 1.5915 | 0.5990 | 600 | 1.2327 |
| 1.5915 | 0.6988 | 700 | 1.2152 |
| 1.5915 | 0.7987 | 800 | 1.2012 |
| 1.5915 | 0.8985 | 900 | 1.2002 |
| 1.3946 | 0.9983 | 1000 | 1.1854 |
| 1.3946 | 1.0982 | 1100 | 1.1824 |
| 1.3946 | 1.1980 | 1200 | 1.1723 |
| 1.3946 | 1.2979 | 1300 | 1.1589 |
| 1.3946 | 1.3977 | 1400 | 1.1490 |
| 1.321 | 1.4975 | 1500 | 1.1387 |
| 1.321 | 1.5974 | 1600 | 1.1356 |
| 1.321 | 1.6972 | 1700 | 1.1252 |
| 1.321 | 1.7970 | 1800 | 1.1259 |
| 1.321 | 1.8969 | 1900 | 1.1182 |
| 1.2735 | 1.9967 | 2000 | 1.1144 |
| 1.2735 | 2.0965 | 2100 | 1.0966 |
| 1.2735 | 2.1964 | 2200 | 1.1005 |
| 1.2735 | 2.2962 | 2300 | 1.0952 |
| 1.2735 | 2.3960 | 2400 | 1.0935 |
| 1.235 | 2.4959 | 2500 | 1.0840 |
| 1.235 | 2.5957 | 2600 | 1.0766 |
| 1.235 | 2.6955 | 2700 | 1.0719 |
| 1.235 | 2.7954 | 2800 | 1.0665 |
| 1.235 | 2.8952 | 2900 | 1.0644 |
| 1.1954 | 2.9950 | 3000 | 1.0656 |
| 1.1954 | 3.0949 | 3100 | 1.0574 |
| 1.1954 | 3.1947 | 3200 | 1.0495 |
| 1.1954 | 3.2945 | 3300 | 1.0475 |
| 1.1954 | 3.3944 | 3400 | 1.0452 |
| 1.1707 | 3.4942 | 3500 | 1.0399 |
| 1.1707 | 3.5940 | 3600 | 1.0363 |
| 1.1707 | 3.6939 | 3700 | 1.0291 |
| 1.1707 | 3.7937 | 3800 | 1.0338 |
| 1.1707 | 3.8936 | 3900 | 1.0348 |
| 1.1509 | 3.9934 | 4000 | 1.0319 |
| 1.1509 | 4.0932 | 4100 | 1.0219 |
| 1.1509 | 4.1931 | 4200 | 1.0214 |
| 1.1509 | 4.2929 | 4300 | 1.0161 |
| 1.1509 | 4.3927 | 4400 | 1.0158 |
| 1.1275 | 4.4926 | 4500 | 1.0153 |
| 1.1275 | 4.5924 | 4600 | 1.0067 |
| 1.1275 | 4.6922 | 4700 | 1.0058 |
| 1.1275 | 4.7921 | 4800 | 1.0097 |
| 1.1275 | 4.8919 | 4900 | 1.0037 |
| 1.1127 | 4.9917 | 5000 | 1.0048 |
| 1.1127 | 5.0916 | 5100 | 1.0022 |
| 1.1127 | 5.1914 | 5200 | 0.9947 |
| 1.1127 | 5.2912 | 5300 | 0.9947 |
| 1.1127 | 5.3911 | 5400 | 0.9907 |
| 1.0944 | 5.4909 | 5500 | 0.9909 |
| 1.0944 | 5.5907 | 5600 | 0.9861 |
| 1.0944 | 5.6906 | 5700 | 0.9858 |
| 1.0944 | 5.7904 | 5800 | 0.9861 |
| 1.0944 | 5.8902 | 5900 | 0.9791 |
| 1.0847 | 5.9901 | 6000 | 0.9787 |
| 1.0847 | 6.0899 | 6100 | 0.9744 |
| 1.0847 | 6.1897 | 6200 | 0.9752 |
| 1.0847 | 6.2896 | 6300 | 0.9712 |
| 1.0847 | 6.3894 | 6400 | 0.9723 |
| 1.0662 | 6.4893 | 6500 | 0.9706 |
| 1.0662 | 6.5891 | 6600 | 0.9688 |
| 1.0662 | 6.6889 | 6700 | 0.9692 |
| 1.0662 | 6.7888 | 6800 | 0.9655 |
| 1.0662 | 6.8886 | 6900 | 0.9637 |
| 1.0559 | 6.9884 | 7000 | 0.9629 |
| 1.0559 | 7.0883 | 7100 | 0.9618 |
| 1.0559 | 7.1881 | 7200 | 0.9622 |
| 1.0559 | 7.2879 | 7300 | 0.9605 |
| 1.0559 | 7.3878 | 7400 | 0.9560 |
| 1.0439 | 7.4876 | 7500 | 0.9562 |
| 1.0439 | 7.5874 | 7600 | 0.9566 |
| 1.0439 | 7.6873 | 7700 | 0.9515 |
| 1.0439 | 7.7871 | 7800 | 0.9514 |
| 1.0439 | 7.8869 | 7900 | 0.9542 |
| 1.0358 | 7.9868 | 8000 | 0.9504 |
| 1.0358 | 8.0866 | 8100 | 0.9502 |
| 1.0358 | 8.1864 | 8200 | 0.9494 |
| 1.0358 | 8.2863 | 8300 | 0.9451 |
| 1.0358 | 8.3861 | 8400 | 0.9461 |
| 1.0242 | 8.4859 | 8500 | 0.9447 |
| 1.0242 | 8.5858 | 8600 | 0.9455 |
| 1.0242 | 8.6856 | 8700 | 0.9441 |
| 1.0242 | 8.7854 | 8800 | 0.9399 |
| 1.0242 | 8.8853 | 8900 | 0.9410 |
| 1.0198 | 8.9851 | 9000 | 0.9391 |
| 1.0198 | 9.0850 | 9100 | 0.9390 |
| 1.0198 | 9.1848 | 9200 | 0.9379 |
| 1.0198 | 9.2846 | 9300 | 0.9382 |
| 1.0198 | 9.3845 | 9400 | 0.9377 |
| 1.0094 | 9.4843 | 9500 | 0.9363 |
| 1.0094 | 9.5841 | 9600 | 0.9354 |
| 1.0094 | 9.6840 | 9700 | 0.9353 |
| 1.0094 | 9.7838 | 9800 | 0.9351 |
| 1.0094 | 9.8836 | 9900 | 0.9342 |
| 1.011 | 9.9835 | 10000 | 0.9339 |
Framework versions
- Transformers 4.43.4
- Pytorch 2.1.1+cu121
- Datasets 3.0.2
- Tokenizers 0.19.1
- Type
- xlm-roberta
- License
- mit
- Created
- Oct 28, 2024
- Updated
- Oct 28, 2024