# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("brugmark/all-MiniLM-L6-v2-personal-project-finetuned-2024-02-16")
model = AutoModelForMaskedLM.from_pretrained("brugmark/all-MiniLM-L6-v2-personal-project-finetuned-2024-02-16")Quick Links
all-MiniLM-L6-v2-personal-project-finetuned-2024-02-16
This model is a fine-tuned version of sentence-transformers/all-MiniLM-L6-v2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 7.9426
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: 32
- eval_batch_size: 32
- 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 |
|---|---|---|---|
| 10.1328 | 1.0 | 10 | 8.9404 |
| 8.683 | 2.0 | 20 | 8.1479 |
| 8.1871 | 3.0 | 30 | 7.8935 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
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Model tree for brugmark/all-MiniLM-L6-v2-personal-project-finetuned-2024-02-16
Base model
nreimers/MiniLM-L6-H384-uncased Quantized
sentence-transformers/all-MiniLM-L6-v2
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="brugmark/all-MiniLM-L6-v2-personal-project-finetuned-2024-02-16")