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# sd-ner-v2

This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract) on the source_data_nlp dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1551
- Accuracy Score: 0.9513
- Precision: 0.8030
- Recall: 0.8378
- F1: 0.8200

## 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: 0.0001
- train_batch_size: 64
- eval_batch_size: 256
- seed: 42
- optimizer: Adafactor
- lr_scheduler_type: linear
- num_epochs: 2.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy Score | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:---------:|:------:|:------:|
| 0.1082        | 1.0   | 785  | 0.1550          | 0.9493         | 0.7826    | 0.8402 | 0.8104 |
| 0.073         | 2.0   | 1570 | 0.1551          | 0.9513         | 0.8030    | 0.8378 | 0.8200 |


### Framework versions

- Transformers 4.20.0
- Pytorch 1.11.0a0+bfe5ad2
- Datasets 1.17.0
- Tokenizers 0.12.1

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["source_data_nlp"], "metrics": ["precision", "recall", "f1", {"name": "sd-ner-v2", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "source_data_nlp", "type": "source_data_nlp", "args": "NER"}, "metrics": [{"name": "Precision", "type": "precision", "value": 0.8030010681183889}, {"name": "Recall", "type": "recall", "value": 0.837754771918473}, {"name": "F1", "type": "f1", "value": 0.8200098518700961}]}]}], "widget": [{"text": "Confocal images of Bmm-GFP (green) in 3rd instar larval fat bodies of different genotypes. DAPI (blue) stains nuclei. Scale bar represents 25 \u00b5m. (A) Knocking down CSN2 or overexpressing RDH/CG2064 in animals with DGAT1 overexpression (ppl>DGAT1) decreases the level and lipid droplet localization of Bmm-GFP."}, {"text": "The GFP intensity along the line across a lipid droplet in (A) was measured by ImageJ.The lipid droplet localization of Bmm-GFP, represented by two peaks, is clearly visible in fat cells from ppl > DGAT1 larvae , but it is lost in fat cells from ppl > DGAT1 larvae with CSN2 RNAi or overexpression of RDH/CG2064. More than 30 lipid droplets of each genotype were measured. One typical image curve is shown for each genotype."}, {"text": "XPT of siRNA treated DC3. 2R cells after 48 hours of knockdown. Treated cells were fed with the indicated amounts of C8L peptid conjugated to iron oxide beads via a disulfide bond. The cells were then exposed to RF33. 70-Luc Reporter CD8 T cells overnight. Error bars show SD of >3 replicate wells. * p<0.05 for siRNA vs control I-Ab using two-way ANOVA. Representative plot of 3 independent experiments."}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer dataset:source_data_nlp license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-source_data_nlp #license-mit #autotrain_compatible #endpoints_compatible #region-us 

sd-ner-v2
=========


This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract on the source\_data\_nlp dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1551
* Accuracy Score: 0.9513
* Precision: 0.8030
* Recall: 0.8378
* F1: 0.8200


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: 0.0001
* train\_batch\_size: 64
* eval\_batch\_size: 256
* seed: 42
* optimizer: Adafactor
* lr\_scheduler\_type: linear
* num\_epochs: 2.0


### Training results



### Framework versions


* Transformers 4.20.0
* Pytorch 1.11.0a0+bfe5ad2
* Datasets 1.17.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 64
* eval\_batch\_size: 256
* seed: 42
* optimizer: Adafactor
* lr\_scheduler\_type: linear
* num\_epochs: 2.0 ### Training results ### Framework versions


* Transformers 4.20.0
* Pytorch 1.11.0a0+bfe5ad2
* Datasets 1.17.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-source_data_nlp #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 64
* eval\_batch\_size: 256
* seed: 42
* optimizer: Adafactor
* lr\_scheduler\_type: linear
* num\_epochs: 2.0 ### Training results ### Framework versions


* Transformers 4.20.0
* Pytorch 1.11.0a0+bfe5ad2
* Datasets 1.17.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-source_data_nlp #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 64
* eval\_batch\_size: 256
* seed: 42
* optimizer: Adafactor
* lr\_scheduler\_type: linear
* num\_epochs: 2.0### Training results### Framework versions


* Transformers 4.20.0
* Pytorch 1.11.0a0+bfe5ad2
* Datasets 1.17.0
* Tokenizers 0.12.1