# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("gngpostalsrvc/BERiT_2000_enriched")
model = AutoModelForMaskedLM.from_pretrained("gngpostalsrvc/BERiT_2000_enriched")Quick Links
BERiT_2000_enriched
This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 6.6052
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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 6.786 | 0.19 | 500 | 6.6797 |
| 6.6441 | 0.39 | 1000 | 6.6574 |
| 6.6376 | 0.58 | 1500 | 6.6240 |
| 6.5951 | 0.77 | 2000 | 6.6291 |
| 6.6123 | 0.97 | 2500 | 6.6355 |
| 6.6028 | 1.16 | 3000 | 6.6084 |
| 6.5974 | 1.36 | 3500 | 6.5984 |
| 6.6104 | 1.55 | 4000 | 6.5775 |
| 6.6113 | 1.74 | 4500 | 6.6062 |
| 6.5895 | 1.94 | 5000 | 6.5931 |
| 6.6106 | 2.13 | 5500 | 6.6276 |
| 6.635 | 2.32 | 6000 | 6.5973 |
| 6.5694 | 2.52 | 6500 | 6.6021 |
| 6.612 | 2.71 | 7000 | 6.5882 |
| 6.5984 | 2.9 | 7500 | 6.6052 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="gngpostalsrvc/BERiT_2000_enriched")