# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("alexandro767/mT0_for_token_classification")
model = AutoModelForTokenClassification.from_pretrained("alexandro767/mT0_for_token_classification")Quick Links
alexandro767/mT0-base-toxic-spans-detection-baseline-9L
This model is a fine-tuned version of bigscience/mt0-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0720
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: 9e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 256
- optimizer: Use adafactor and the args are: No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 4
Training results
Framework versions
- Transformers 4.48.3
- Pytorch 2.2.0
- Datasets 3.3.0
- Tokenizers 0.21.0
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Model tree for alexandro767/mT0_for_token_classification
Base model
bigscience/mt0-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="alexandro767/mT0_for_token_classification")