File size: 2,728 Bytes
86bc393 f6408e8 86bc393 f6408e8 86bc393 f6408e8 86bc393 f6408e8 86bc393 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 |
---
language:
- en
tags:
- generated_from_trainer
datasets:
- mnli
metrics:
- accuracy
base_model: allenai/scibert_scivocab_uncased
model-index:
- name: glue
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: GLUE MNLI
type: glue
args: mnli
metrics:
- type: accuracy
value: 0.834519934906428
name: Accuracy
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mnli
This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4917
- Accuracy: 0.8345
## Model description
This is the pretrained model presented in [SciBERT: A Pretrained Language Model for Scientific Text](https://www.aclweb.org/anthology/D19-1371/), which is a BERT model trained on scientific text, then finetuned on GLUE MNLI for zero-shot classification.
The training corpus was papers taken from [Semantic Scholar](https://www.semanticscholar.org). Corpus size is 1.14M papers, 3.1B tokens. We use the full text of the papers in training, not just abstracts.
SciBERT has its own wordpiece vocabulary (scivocab) that's built to best match the training corpus.
## Intended uses & limitations
Zero-shot classification of scientific texts. Note that this model is outperformed by multiple models and was uploaded for research purposes. For actually classifying scientific text, I recommend looking into [Deberta v3 Large tuned on MNLI](https://huggingface.co/navteca/nli-deberta-v3-large) which according to my benchmark on abstracts performs best at current date (7/10/22).
## Training and evaluation data
GLUE MNLI
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.22.2
- Pytorch 1.11.0+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
If using these models, please cite the following paper:
```
@inproceedings{beltagy-etal-2019-scibert,
title = "SciBERT: A Pretrained Language Model for Scientific Text",
author = "Beltagy, Iz and Lo, Kyle and Cohan, Arman",
booktitle = "EMNLP",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D19-1371"
}
``` |