# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Pastushoc/rubert-tiny2-finetuned-fintech")
model = AutoModelForMaskedLM.from_pretrained("Pastushoc/rubert-tiny2-finetuned-fintech", device_map="auto")Quick Links
rubert-tiny2-finetuned-fintech
This model is a fine-tuned version of cointegrated/rubert-tiny2 on the generator dataset. It achieves the following results on the evaluation set:
- Loss: 5.3573
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 5.698 | 1.0 | 2069 | 5.4277 |
| 5.5758 | 2.0 | 4138 | 5.3573 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for Pastushoc/rubert-tiny2-finetuned-fintech
Base model
cointegrated/rubert-tiny2
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Pastushoc/rubert-tiny2-finetuned-fintech")