# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("amanm27/bert-base-uncased-scouting")
model = AutoModelForMaskedLM.from_pretrained("amanm27/bert-base-uncased-scouting")Quick Links
bert-base-uncased-scouting
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.5443
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: 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: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 378 | 1.7727 |
| 2.1016 | 2.0 | 756 | 1.6040 |
| 1.7298 | 3.0 | 1134 | 1.5572 |
Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0
- Datasets 1.18.3
- Tokenizers 0.11.0
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="amanm27/bert-base-uncased-scouting")