File size: 1,737 Bytes
96a1b0a 421264a a0be7cd 96a1b0a 421264a a0be7cd 421264a a0be7cd 421264a a0be7cd 96a1b0a 421264a 96a1b0a |
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 |
---
license: mit
tags:
- generated_from_trainer
datasets:
- enoriega/keyword_pubmed
metrics:
- accuracy
base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext
model-index:
- name: kw_pubmed_vanilla_sentence_10000_0.0003_2
results:
- task:
type: fill-mask
name: Masked Language Modeling
dataset:
name: enoriega/keyword_pubmed sentence
type: enoriega/keyword_pubmed
args: sentence
metrics:
- type: accuracy
value: 0.6767448105720579
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. -->
# kw_pubmed_vanilla_sentence_10000_0.0003_2
This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the enoriega/keyword_pubmed sentence dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5883
- Accuracy: 0.6767
## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 500
- total_train_batch_size: 8000
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|