# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Ehsanl/me5-small-trimmed-old-syn-filt_2ng_llr")
model = AutoModel.from_pretrained("Ehsanl/me5-small-trimmed-old-syn-filt_2ng_llr", device_map="auto")Quick Links
me5-small-trimmed-old-syn-filt_2ng_llr
This model is a fine-tuned version of nicolaebanari/me5-small-trimmed-nl-test on an unknown dataset.
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: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 500
- num_epochs: 1.0
Training results
Framework versions
- Transformers 4.56.1
- Pytorch 2.5.1+cu124
- Datasets 4.0.0
- Tokenizers 0.22.0
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Ehsanl/me5-small-trimmed-old-syn-filt_2ng_llr")