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# wikigold_trained_no_DA_small

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the wikigold_splits dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6066
- Precision: 0.3429
- Recall: 0.5455
- F1: 0.4211
- Accuracy: 0.8530

## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 32

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 9    | 0.8525          | 0.0       | 0.0    | 0.0    | 0.7604   |
| No log        | 2.0   | 18   | 0.7135          | 0.0       | 0.0    | 0.0    | 0.7604   |
| No log        | 3.0   | 27   | 0.5972          | 0.1579    | 0.1364 | 0.1463 | 0.7923   |
| No log        | 4.0   | 36   | 0.5108          | 0.0769    | 0.0909 | 0.0833 | 0.8083   |
| No log        | 5.0   | 45   | 0.4725          | 0.2333    | 0.3182 | 0.2692 | 0.8466   |
| No log        | 6.0   | 54   | 0.4569          | 0.2333    | 0.3182 | 0.2692 | 0.8339   |
| No log        | 7.0   | 63   | 0.4428          | 0.2258    | 0.3182 | 0.2642 | 0.8371   |
| No log        | 8.0   | 72   | 0.4362          | 0.2121    | 0.3182 | 0.2545 | 0.8435   |
| No log        | 9.0   | 81   | 0.4509          | 0.2258    | 0.3182 | 0.2642 | 0.8403   |
| No log        | 10.0  | 90   | 0.4614          | 0.2121    | 0.3182 | 0.2545 | 0.8466   |
| No log        | 11.0  | 99   | 0.4546          | 0.2188    | 0.3182 | 0.2593 | 0.8435   |
| No log        | 12.0  | 108  | 0.4734          | 0.2188    | 0.3182 | 0.2593 | 0.8435   |
| No log        | 13.0  | 117  | 0.5098          | 0.2581    | 0.3636 | 0.3019 | 0.8466   |
| No log        | 14.0  | 126  | 0.5280          | 0.2258    | 0.3182 | 0.2642 | 0.8435   |
| No log        | 15.0  | 135  | 0.5264          | 0.2188    | 0.3182 | 0.2593 | 0.8435   |
| No log        | 16.0  | 144  | 0.5317          | 0.2727    | 0.4091 | 0.3273 | 0.8498   |
| No log        | 17.0  | 153  | 0.5414          | 0.2581    | 0.3636 | 0.3019 | 0.8466   |
| No log        | 18.0  | 162  | 0.5505          | 0.2581    | 0.3636 | 0.3019 | 0.8466   |
| No log        | 19.0  | 171  | 0.5521          | 0.2581    | 0.3636 | 0.3019 | 0.8466   |
| No log        | 20.0  | 180  | 0.5627          | 0.2581    | 0.3636 | 0.3019 | 0.8466   |
| No log        | 21.0  | 189  | 0.5687          | 0.2581    | 0.3636 | 0.3019 | 0.8466   |
| No log        | 22.0  | 198  | 0.5751          | 0.2581    | 0.3636 | 0.3019 | 0.8466   |
| No log        | 23.0  | 207  | 0.5825          | 0.2727    | 0.4091 | 0.3273 | 0.8498   |
| No log        | 24.0  | 216  | 0.5881          | 0.2727    | 0.4091 | 0.3273 | 0.8498   |
| No log        | 25.0  | 225  | 0.5930          | 0.2727    | 0.4091 | 0.3273 | 0.8498   |
| No log        | 26.0  | 234  | 0.5969          | 0.2727    | 0.4091 | 0.3273 | 0.8498   |
| No log        | 27.0  | 243  | 0.5995          | 0.3429    | 0.5455 | 0.4211 | 0.8530   |
| No log        | 28.0  | 252  | 0.6017          | 0.3429    | 0.5455 | 0.4211 | 0.8530   |
| No log        | 29.0  | 261  | 0.6035          | 0.3429    | 0.5455 | 0.4211 | 0.8530   |
| No log        | 30.0  | 270  | 0.6053          | 0.3429    | 0.5455 | 0.4211 | 0.8530   |
| No log        | 31.0  | 279  | 0.6063          | 0.3429    | 0.5455 | 0.4211 | 0.8530   |
| No log        | 32.0  | 288  | 0.6066          | 0.3429    | 0.5455 | 0.4211 | 0.8530   |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wikigold_splits"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "wikigold_trained_no_DA_small", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wikigold_splits", "type": "wikigold_splits", "args": "default"}, "metrics": [{"type": "precision", "value": 0.34285714285714286, "name": "Precision"}, {"type": "recall", "value": 0.5454545454545454, "name": "Recall"}, {"type": "f1", "value": 0.42105263157894735, "name": "F1"}, {"type": "accuracy", "value": 0.853035143769968, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer dataset:wikigold_splits license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-wikigold_splits #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

wikigold\_trained\_no\_DA\_small
================================


This model is a fine-tuned version of bert-base-cased on the wikigold\_splits dataset.
It achieves the following results on the evaluation set:


* Loss: 0.6066
* Precision: 0.3429
* Recall: 0.5455
* F1: 0.4211
* Accuracy: 0.8530


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: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 32


### Training results



### Framework versions


* Transformers 4.17.0
* Pytorch 1.11.0+cu113
* Datasets 2.4.0
* Tokenizers 0.11.6



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 32 ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.11.0+cu113
* Datasets 2.4.0
* Tokenizers 0.11.6
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-wikigold_splits #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: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 32 ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.11.0+cu113
* Datasets 2.4.0
* Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-wikigold_splits #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: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 32### Training results### Framework versions


* Transformers 4.17.0
* Pytorch 1.11.0+cu113
* Datasets 2.4.0
* Tokenizers 0.11.6