Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use mariolinml/roberta-base_mnli_uf_ner_1024_train_v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mariolinml/roberta-base_mnli_uf_ner_1024_train_v0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mariolinml/roberta-base_mnli_uf_ner_1024_train_v0")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mariolinml/roberta-base_mnli_uf_ner_1024_train_v0") model = AutoModelForSequenceClassification.from_pretrained("mariolinml/roberta-base_mnli_uf_ner_1024_train_v0") - Notebooks
- Google Colab
- Kaggle
roberta-base_mnli_uf_ner_1024_train_v0
This model is a fine-tuned version of mariolinml/roberta-base_fullMnli_10_24_v0 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
Training results
Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
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