Feature Extraction
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Ehsanl/Robbert_base23_old_7neg_kd")
model = AutoModel.from_pretrained("Ehsanl/Robbert_base23_old_7neg_kd")Quick Links
Robbert_base23_old_7neg_kd
This model is a fine-tuned version of FremyCompany/roberta-base-nl-oscar23 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
Training results
Framework versions
- Transformers 4.55.4
- Pytorch 2.7.1+cu128
- Datasets 4.0.0
- Tokenizers 0.21.4
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Model tree for Ehsanl/Robbert_base23_old_7neg_kd
Base model
FremyCompany/roberta-base-nl-oscar23
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Ehsanl/Robbert_base23_old_7neg_kd")