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# scibert_scivocab_uncased-finetuned-ner

This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the plo_dunfiltered_config dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1390
- Precision: 0.9649
- Recall: 0.9612
- F1: 0.9631
- Accuracy: 0.9594

## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 11

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1176        | 1.4   | 5000  | 0.1243          | 0.9570    | 0.9511 | 0.9540 | 0.9502   |
| 0.0973        | 2.81  | 10000 | 0.1129          | 0.9609    | 0.9572 | 0.9590 | 0.9553   |
| 0.0721        | 4.21  | 15000 | 0.1198          | 0.9645    | 0.9585 | 0.9615 | 0.9578   |
| 0.0634        | 5.62  | 20000 | 0.1259          | 0.9649    | 0.9589 | 0.9619 | 0.9582   |
| 0.0572        | 7.02  | 25000 | 0.1321          | 0.9653    | 0.9609 | 0.9631 | 0.9594   |
| 0.0472        | 8.43  | 30000 | 0.1390          | 0.9649    | 0.9612 | 0.9631 | 0.9594   |
| 0.0434        | 9.83  | 35000 | 0.1442          | 0.9656    | 0.9613 | 0.9634 | 0.9598   |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.1+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1

{"tags": ["generated_from_trainer"], "datasets": ["plo_dunfiltered_config"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "scibert_scivocab_uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "plo_dunfiltered_config", "type": "plo_dunfiltered_config", "args": "PLODunfiltered"}, "metrics": [{"type": "precision", "value": 0.964925429790286, "name": "Precision"}, {"type": "recall", "value": 0.9612323892385586, "name": "Recall"}, {"type": "f1", "value": 0.9630753691636831, "name": "F1"}, {"type": "accuracy", "value": 0.9593916827485913, "name": "Accuracy"}]}]}]}
transformers pytorch bert token-classification generated_from_trainer dataset:plo_dunfiltered_config model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-plo_dunfiltered_config #model-index #autotrain_compatible #endpoints_compatible #region-us 

scibert\_scivocab\_uncased-finetuned-ner
========================================


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


* Loss: 0.1390
* Precision: 0.9649
* Recall: 0.9612
* F1: 0.9631
* Accuracy: 0.9594


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: 1e-05
* train\_batch\_size: 32
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 11


### Training results



### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.1+cu111
* Datasets 2.1.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 32
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 11 ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.1+cu111
* Datasets 2.1.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-plo_dunfiltered_config #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 32
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 11 ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.1+cu111
* Datasets 2.1.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-plo_dunfiltered_config #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 32
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 11### Training results### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.1+cu111
* Datasets 2.1.0
* Tokenizers 0.12.1