Parameters—
Context514
Weights1.5 GB
License—
AccessOpen weights
Monthly Downloads2k
Model Card
The publisher has not written a card for this model.
Configuration
- Architecture
- RobertaForQuestionAnswering
- Context length (tokens)
- 514
- Layers
- 12
- Hidden size
- 768
- Feed-forward size
- 3,072
- Attention heads
- 12
- Vocabulary size
- 50,265
- Stored precision
- float32
- Model type
- roberta
Identity and Version
- Repository
- anablasi/finqa_model
- Publisher
- Ana Blasi
- Task
- Question answering
- Modality
- Text
- Library
- transformers
- Parameters
- Not stated by the source
- Languages
- Not stated by the source
- Revision
- 97c67dba169cfe91315c073d7c509e2107f29fe7
- First published
- 2022-05-24
- Last updated
- 2022-05-24
Files and Weights
13 files, 1.5 GB in total. The weights are 5 files totalling 1.5 GB in bin, pt, pth.
Weights5 files · 1.5 GB
Configuration3 files · 4.3 KB
Tokenizer4 files · 3.4 MB
Repository1 file · 1.2 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| optimizer.pt | Weights | 992.6 MB | de103bc257eb |
| pytorch_model.bin | Weights | 496.3 MB | 2c370d544340 |
| rng_state.pth | Weights | 14.5 KB | 387e7bdcfeec |
| scheduler.pt | Weights | 623 B | a36b776c2410 |
| training_args.bin | Weights | 3.2 KB | 924ad7d54f2b |
| config.json | Configuration | 774 B | — |
| special_tokens_map.json | Configuration | 772 B | — |
| trainer_state.json | Configuration | 2.7 KB | — |
| .gitattributes | Repository | 1.2 KB | — |
| merges.txt | Tokenizer | 456.4 KB | — |
| tokenizer.json | Tokenizer | 2.1 MB | — |
| tokenizer_config.json | Tokenizer | 1.4 KB | — |
| vocab.json | Tokenizer | 798.3 KB | — |
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
- Download size
- 1.5 GB
Download from Ana Blasi
Released by Ana Blasi through its official repository on Hugging Face.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 1.5 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About finqa_model
What is finqa_model's context length?
514 tokens, from the maximum position embeddings in its published configuration.
Similar Models
Splinter-base is the pretrained model discussed in the paper Few-Shot Question Answering by Pretraining Span Selection (at ACL 2021). Its original repository can be found here. The model is case-sensitive. Note: This model doesn't contain the pretrained weights for the QASS layer (see paper for details), and therefore the QASS layer is randomly initialized upon loading it. For the model with those weights, see tau/splinter-base-qass. Splinter is a model that is pretrained in a self-supervised fashion for few-shot question answering. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an…
This model can be used for the task of question answering. The model should not be used to intentionally create hostile or alienating environments for people. Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. The model creators note in the associated paper: The model creators note in the associated paper: The model…
This model is a fine-tune checkpoint of DistilBERT-base-cased, fine-tuned using (a second step of) knowledge distillation on SQuAD v1.1. This model reaches a F1 score of 87.1 on the dev set (for comparison, BERT bert-base-cased version reaches a F1 score of 88.7).
This model can be used for the task of Question Answering on Legal Documents. Read: An Open Source Contractual Language Understanding Application Using Machine Learning for detailed information on training procedure, dataset preprocessing and evaluation. See CUAD dataset card for more information. See CUAD dataset card for more information. Used V100/P100 from Google Colab Pro Python, Transformers Mohammed Rakib in collaboration with Ezi Ozoani and the Hugging Face team Use the code below to get started with the model.