Instructions to use Evelyn18/roberta-base-bne-ROBERTaBECAS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Evelyn18/roberta-base-bne-ROBERTaBECAS with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="Evelyn18/roberta-base-bne-ROBERTaBECAS")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Evelyn18/roberta-base-bne-ROBERTaBECAS") model = AutoModelForQuestionAnswering.from_pretrained("Evelyn18/roberta-base-bne-ROBERTaBECAS", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Evelyn18/roberta-base-bne-ROBERTaBECAS: direct link, hf CLI and curl.
- Browser
- Download file 1.52 kB
-
https://huggingface.co/Evelyn18/roberta-base-bne-ROBERTaBECAS/resolve/main/README.md
- Command line
-
hf download hf://Evelyn18/roberta-base-bne-ROBERTaBECAS/README.md
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curl -L -o README.md https://huggingface.co/Evelyn18/roberta-base-bne-ROBERTaBECAS/resolve/main/README.md
1.52 kB
metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- becasv2
model-index:
- name: roberta-base-bne-ROBERTaBECAS
results: []
roberta-base-bne-ROBERTaBECAS
This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the becasv2 dataset. It achieves the following results on the evaluation set:
- Loss: 2.5760
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: 11
- eval_batch_size: 11
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 6 | 4.3366 |
| No log | 2.0 | 12 | 3.1395 |
| No log | 3.0 | 18 | 2.6092 |
| No log | 4.0 | 24 | 2.5084 |
| No log | 5.0 | 30 | 2.5760 |
Framework versions
- Transformers 4.21.1
- Pytorch 1.9.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1