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# scibert-finetuned-ner

This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the jnlpba dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4717
- Precision: 0.6737
- Recall: 0.7757
- F1: 0.7211
- Accuracy: 0.9226

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1608        | 1.0   | 2319  | 0.2431          | 0.6641    | 0.7581 | 0.7080 | 0.9250   |
| 0.103         | 2.0   | 4638  | 0.2916          | 0.6739    | 0.7803 | 0.7232 | 0.9228   |
| 0.0659        | 3.0   | 6957  | 0.3662          | 0.6796    | 0.7624 | 0.7186 | 0.9233   |
| 0.0393        | 4.0   | 9276  | 0.4222          | 0.6737    | 0.7771 | 0.7217 | 0.9225   |
| 0.025         | 5.0   | 11595 | 0.4717          | 0.6737    | 0.7757 | 0.7211 | 0.9226   |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"tags": ["generated_from_trainer"], "datasets": ["jnlpba"], "metrics": ["precision", "recall", "f1", "accuracy"], "widget": [{"text": "The widespread circular form of DNA molecules inside cells creates very serious topological problems during replication. Due to the helical structure of the double helix the parental strands of circular DNA form a link of very high order, and yet they have to be unlinked before the cell division."}, {"text": "It consists of 25 exons encoding a 1,278-amino acid glycoprotein that is composed of 13 transmembrane domains"}], "model-index": [{"name": "scibert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "jnlpba", "type": "jnlpba", "config": "jnlpba", "split": "train", "args": "jnlpba"}, "metrics": [{"type": "precision", "value": 0.6737190414118119, "name": "Precision"}, {"type": "recall", "value": 0.7756869083352574, "name": "Recall"}, {"type": "f1", "value": 0.7211161792326267, "name": "F1"}, {"type": "accuracy", "value": 0.9226268866380928, "name": "Accuracy"}]}]}]}
transformers pytorch bert token-classification generated_from_trainer dataset:jnlpba model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-jnlpba #model-index #autotrain_compatible #endpoints_compatible #region-us 

scibert-finetuned-ner
=====================


This model is a fine-tuned version of allenai/scibert\_scivocab\_uncased on the jnlpba dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4717
* Precision: 0.6737
* Recall: 0.7757
* F1: 0.7211
* Accuracy: 0.9226


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: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5


### Training results



### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-jnlpba #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-jnlpba #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5### Training results### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1