
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# canine_2303

This model is a fine-tuned version of [google/canine-s](https://huggingface.co/google/canine-s) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0001
- Precision: 0.9987
- Recall: 0.9982
- F1: 0.9985
- Accuracy: 0.9999

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 244  | 0.0025          | 0.9819    | 0.9924 | 0.9871 | 0.9993   |
| No log        | 2.0   | 488  | 0.0018          | 0.9855    | 0.9925 | 0.9890 | 0.9995   |
| 0.0382        | 3.0   | 732  | 0.0014          | 0.9923    | 0.9891 | 0.9907 | 0.9996   |
| 0.0382        | 4.0   | 976  | 0.0009          | 0.9930    | 0.9931 | 0.9931 | 0.9997   |
| 0.0017        | 5.0   | 1220 | 0.0009          | 0.9922    | 0.9949 | 0.9936 | 0.9997   |
| 0.0017        | 6.0   | 1464 | 0.0007          | 0.9940    | 0.9952 | 0.9946 | 0.9998   |
| 0.0012        | 7.0   | 1708 | 0.0005          | 0.9947    | 0.9952 | 0.9949 | 0.9998   |
| 0.0012        | 8.0   | 1952 | 0.0005          | 0.9947    | 0.9955 | 0.9951 | 0.9998   |
| 0.0009        | 9.0   | 2196 | 0.0003          | 0.9959    | 0.9960 | 0.9959 | 0.9998   |
| 0.0009        | 10.0  | 2440 | 0.0003          | 0.9958    | 0.9963 | 0.9961 | 0.9998   |
| 0.0007        | 11.0  | 2684 | 0.0003          | 0.9971    | 0.9958 | 0.9965 | 0.9999   |
| 0.0007        | 12.0  | 2928 | 0.0003          | 0.9971    | 0.9962 | 0.9967 | 0.9999   |
| 0.0005        | 13.0  | 3172 | 0.0002          | 0.9974    | 0.9967 | 0.9971 | 0.9999   |
| 0.0005        | 14.0  | 3416 | 0.0002          | 0.9980    | 0.9972 | 0.9976 | 0.9999   |
| 0.0004        | 15.0  | 3660 | 0.0002          | 0.9982    | 0.9980 | 0.9981 | 0.9999   |
| 0.0004        | 16.0  | 3904 | 0.0002          | 0.9984    | 0.9974 | 0.9979 | 0.9999   |
| 0.0004        | 17.0  | 4148 | 0.0001          | 0.9984    | 0.9975 | 0.9979 | 0.9999   |
| 0.0004        | 18.0  | 4392 | 0.0001          | 0.9988    | 0.9982 | 0.9985 | 0.9999   |
| 0.0003        | 19.0  | 4636 | 0.0001          | 0.9987    | 0.9982 | 0.9985 | 0.9999   |
| 0.0003        | 20.0  | 4880 | 0.0001          | 0.9987    | 0.9982 | 0.9985 | 0.9999   |


### Framework versions

- Transformers 4.27.2
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "canine_2303", "results": []}]}
transformers pytorch tensorboard canine token-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #canine #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

canine\_2303
============


This model is a fine-tuned version of google/canine-s on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0001
* Precision: 0.9987
* Recall: 0.9982
* F1: 0.9985
* Accuracy: 0.9999


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: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20


### Training results



### Framework versions


* Transformers 4.27.2
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.27.2
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #canine #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.27.2
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #canine #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20### Training results### Framework versions


* Transformers 4.27.2
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2