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# albert-large-v2-finetuned-ner_with_callbacks

This model is a fine-tuned version of [albert-large-v2](https://huggingface.co/albert-large-v2) on the [PLOD-unfiltered](https://huggingface.co/datasets/surrey-nlp/PLOD-unfiltered) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1235
- Precision: 0.9655
- Recall: 0.9608
- F1: 0.9632
- Accuracy: 0.9589

## 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: 8
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1377        | 0.49  | 7000  | 0.1294          | 0.9563    | 0.9422 | 0.9492 | 0.9436   |
| 0.1244        | 0.98  | 14000 | 0.1165          | 0.9589    | 0.9504 | 0.9546 | 0.9499   |
| 0.107         | 1.48  | 21000 | 0.1140          | 0.9603    | 0.9509 | 0.9556 | 0.9511   |
| 0.1088        | 1.97  | 28000 | 0.1086          | 0.9613    | 0.9551 | 0.9582 | 0.9536   |
| 0.0918        | 2.46  | 35000 | 0.1059          | 0.9617    | 0.9582 | 0.9600 | 0.9556   |
| 0.0847        | 2.95  | 42000 | 0.1067          | 0.9620    | 0.9586 | 0.9603 | 0.9559   |
| 0.0734        | 3.44  | 49000 | 0.1188          | 0.9646    | 0.9588 | 0.9617 | 0.9574   |
| 0.0725        | 3.93  | 56000 | 0.1065          | 0.9660    | 0.9599 | 0.9630 | 0.9588   |
| 0.0547        | 4.43  | 63000 | 0.1273          | 0.9662    | 0.9602 | 0.9632 | 0.9590   |
| 0.0542        | 4.92  | 70000 | 0.1235          | 0.9655    | 0.9608 | 0.9632 | 0.9589   |
| 0.0374        | 5.41  | 77000 | 0.1401          | 0.9647    | 0.9613 | 0.9630 | 0.9586   |
| 0.0417        | 5.9   | 84000 | 0.1380          | 0.9641    | 0.9622 | 0.9632 | 0.9588   |


### Framework versions

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

{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["surrey-nlp/PLOD-unfiltered"], "metrics": ["precision", "recall", "f1", "accuracy"], "widget": [{"text": "Light dissolved inorganic carbon (DIC) resulting from the oxidation of hydrocarbons."}, {"text": "RAFs are plotted for a selection of neurons in the dorsal zone (DZ) of auditory cortex in Figure 1."}, {"text": "Images were acquired using a GE 3.0T MRI scanner with an upgrade for echo-planar imaging (EPI)."}], "base_model": "albert-large-v2", "model-index": [{"name": "albert-large-v2-finetuned-ner_with_callbacks", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "surrey-nlp/PLOD-unfiltered", "type": "token-classification", "args": "PLODunfiltered"}, "metrics": [{"type": "precision", "value": 0.9655166719570215, "name": "Precision"}, {"type": "recall", "value": 0.9608483288141474, "name": "Recall"}, {"type": "f1", "value": 0.9631768437660728, "name": "F1"}, {"type": "accuracy", "value": 0.9589410429715819, "name": "Accuracy"}]}]}]}
transformers pytorch safetensors albert token-classification generated_from_trainer en dataset:surrey-nlp/PLOD-unfiltered base_model:albert-large-v2 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #albert #token-classification #generated_from_trainer #en #dataset-surrey-nlp/PLOD-unfiltered #base_model-albert-large-v2 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

albert-large-v2-finetuned-ner\_with\_callbacks
==============================================


This model is a fine-tuned version of albert-large-v2 on the PLOD-unfiltered dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1235
* Precision: 0.9655
* Recall: 0.9608
* F1: 0.9632
* Accuracy: 0.9589


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: 8
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6


### 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: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6 ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.1+cu111
* Datasets 2.1.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #safetensors #albert #token-classification #generated_from_trainer #en #dataset-surrey-nlp/PLOD-unfiltered #base_model-albert-large-v2 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6 ### 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 #safetensors #albert #token-classification #generated_from_trainer #en #dataset-surrey-nlp/PLOD-unfiltered #base_model-albert-large-v2 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6### Training results### Framework versions


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