# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB")
model = AutoModelForMaskedLM.from_pretrained("nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB", device_map="auto")Quick Links
SingleBertModel-ProtBertfinetuned-smilesBindingDB
This model is a fine-tuned version of Rostlab/prot_bert on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: nan
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: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.5245 | 1.0 | 10000 | nan |
| 2.5037 | 2.0 | 20000 | nan |
| 2.4967 | 3.0 | 30000 | nan |
| 2.4983 | 4.0 | 40000 | nan |
| 2.4926 | 5.0 | 50000 | nan |
Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- 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="nepp1d0/SingleBertModel-ProtBertfinetuned-smilesBindingDB")