# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("bthomas/article2keyword2.1b_paraphrase-multilingual-MiniLM-L12-v2_finetuned_for_mlm")
model = AutoModelForMaskedLM.from_pretrained("bthomas/article2keyword2.1b_paraphrase-multilingual-MiniLM-L12-v2_finetuned_for_mlm", device_map="auto")Quick Links
article2keyword2.1b_paraphrase-multilingual-MiniLM-L12-v2_finetuned_for_mlm
This model is a fine-tuned version of sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0673
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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.3777 | 1.0 | 1353 | 0.3168 |
| 0.2358 | 2.0 | 2706 | 0.1564 |
| 0.1372 | 3.0 | 4059 | 0.1149 |
| 0.1046 | 4.0 | 5412 | 0.0956 |
| 0.086 | 5.0 | 6765 | 0.0853 |
| 0.0741 | 6.0 | 8118 | 0.0786 |
| 0.0653 | 7.0 | 9471 | 0.0750 |
| 0.0594 | 8.0 | 10824 | 0.0726 |
| 0.0542 | 9.0 | 12177 | 0.0699 |
| 0.0504 | 10.0 | 13530 | 0.0692 |
| 0.047 | 11.0 | 14883 | 0.0684 |
| 0.0444 | 12.0 | 16236 | 0.0675 |
| 0.0423 | 13.0 | 17589 | 0.0674 |
| 0.0404 | 14.0 | 18942 | 0.0673 |
| 0.0392 | 15.0 | 20295 | 0.0672 |
| 0.0379 | 16.0 | 21648 | 0.0673 |
Framework versions
- Transformers 4.21.1
- Pytorch 1.11.0
- Datasets 2.3.2
- Tokenizers 0.12.1
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="bthomas/article2keyword2.1b_paraphrase-multilingual-MiniLM-L12-v2_finetuned_for_mlm")