Open-weight model · Question answering
bert-base-spanish-wwm-cased-finetuned-spa-squad2-es
by Manuel Romero mrm8488/bert-base-spanish-wwm-cased-finetuned-spa-squad2-es
This model is provided by BETO team and fine-tuned on SQuAD-es-v2.0 for Q&A downstream task. Language model ('dccuchile/bert-base-spanish-wwm-cased'): BETO is a BERT model trained on a big Spanish corpus.
Parameters—
Context512
Weights876.5 MB
License—
AccessOpen weights
Monthly Downloads2.3k
Model Card
This model is provided by BETO team and fine-tuned on SQuAD-es-v2.0 for Q&A downstream task. Language model ('dccuchile/bert-base-spanish-wwm-cased'): BETO is a BERT model trained on a big Spanish corpus. BETO is of size similar to a BERT-Base and was trained with the Whole Word Masking technique. Below you find Tensorflow and Pytorch checkpoints for the uncased and cased versions, as well as some results for Spanish benchmarks comparing BETO with Multilingual BERT as well as other (not BERT-based) models. The model was trained on a Tesla P100 GPU and 25GB of RAM with the following command: 1. Set the context and ask some questions: 2. Run predictions
Excerpt from the card by Manuel Romero.
Configuration
- Architecture
- BertForQuestionAnswering
- Context length (tokens)
- 512
- Layers
- 12
- Hidden size
- 768
- Feed-forward size
- 3,072
- Attention heads
- 12
- Vocabulary size
- 31,002
- Model type
- bert
Identity and Version
- Repository
- mrm8488/bert-base-spanish-wwm-cased-finetuned-spa-squad2-es
- Publisher
- Manuel Romero
- Task
- Question answering
- Modality
- Text
- Library
- transformers
- Parameters
- Not stated by the source
- Languages
- es
- Revision
- 99818221720ac345078458b0b0489d61b21fe137
- First published
- 2022-03-02
- Last updated
- 2021-05-20
Files and Weights
13 files, 1.6 GB in total. The weights are 3 files totalling 876.5 MB in bin, msgpack.
Weights3 files · 876.5 MB
Configuration5 files · 327.9 MB
Tokenizer2 files · 242.5 KB
Documentation1 file · 3.0 KB
Other1 file · 408.0 MB
Repository1 file · 391 B
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| flax_model.msgpack | Weights | 437.1 MB | 0f7ed9d36d9d |
| pytorch_model.bin | Weights | 439.5 MB | 280c5e41b49f |
| training_args.bin | Weights | 1.5 KB | 483b8d0eb374 |
| config.json | Configuration | 465 B | — |
| nbest_predictions_.json | Configuration | 320.9 MB | 085a6a937305 |
| null_odds_.json | Configuration | 3.6 MB | — |
| predictions_.json | Configuration | 3.4 MB | — |
| special_tokens_map.json | Configuration | 112 B | — |
| README.md | Documentation | 3.0 KB | — |
| saved_model.tar.gz | Other | 408.0 MB | 3a6f5510408f |
| .gitattributes | Repository | 391 B | — |
| tokenizer_config.json | Tokenizer | 135 B | — |
| vocab.txt | Tokenizer | 242.3 KB | — |
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
- Download size
- 876.5 MB
Download from Manuel Romero
Released by Manuel Romero through its official repository on Hugging Face.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 876.5 MB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About bert-base-spanish-wwm-cased-finetuned-spa-squad2-es
What is bert-base-spanish-wwm-cased-finetuned-spa-squad2-es's context length?
512 tokens, from the maximum position embeddings in its published configuration.
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