# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("vonewman/results")
model = AutoModelForTokenClassification.from_pretrained("vonewman/results")Quick Links
results
This model is a fine-tuned version of vonewman/wolof-finetuned-ner on the None 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: 5e-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: 1
Training results
Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
- Downloads last month
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Model tree for vonewman/results
Base model
vonewman/wolof-finetuned-ner
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="vonewman/results")