
<!-- 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. -->

# WhisperForNamedEntityRecognition

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the qmeeus/slue-voxpopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 8.1514
- Wer: 10.4828

## 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- training_steps: 1600
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 31.3741       | 0.06  | 100  | 25.8582         | 10.4828 |
| 13.0078       | 1.03  | 200  | 13.4173         | 10.4828 |
| 10.3619       | 1.09  | 300  | 10.8540         | 10.4828 |
| 8.7869        | 2.06  | 400  | 9.6249          | 10.4828 |
| 7.3964        | 3.02  | 500  | 9.1812          | 10.4828 |
| 6.6321        | 3.08  | 600  | 8.6536          | 10.4828 |
| 6.4612        | 4.05  | 700  | 8.6046          | 10.4828 |
| 4.8358        | 5.02  | 800  | 8.0890          | 10.4828 |
| 4.4918        | 5.08  | 900  | 8.3141          | 10.4828 |
| 4.7548        | 6.04  | 1000 | 8.1660          | 10.4828 |
| 3.7881        | 7.01  | 1100 | 8.2471          | 10.4828 |
| 3.1916        | 7.07  | 1200 | 8.0779          | 10.4828 |
| 3.2039        | 8.04  | 1300 | 8.1106          | 10.4828 |
| 3.038         | 9.0   | 1400 | 8.0875          | 10.4828 |
| 2.3249        | 9.07  | 1500 | 8.1025          | 10.4828 |
| 2.6124        | 10.03 | 1600 | 8.1514          | 10.4828 |


### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.10.0
- Datasets 2.7.1.dev0
- Tokenizers 0.11.0

{"license": "apache-2.0", "tags": ["whisper-event", "generated_from_trainer"], "datasets": ["qmeeus/slue-voxpopuli"], "metrics": ["wer"], "base_model": "openai/whisper-small", "model-index": [{"name": "WhisperForNamedEntityRecognition", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "qmeeus/slue-voxpopuli", "type": "qmeeus/slue-voxpopuli", "split": "dev"}, "metrics": [{"type": "wer", "value": 10.482824557809192, "name": "Wer"}]}]}]}
transformers pytorch tensorboard whisper_for_slu token-classification whisper-event generated_from_trainer dataset:qmeeus/slue-voxpopuli base_model:openai/whisper-small license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #whisper_for_slu #token-classification #whisper-event #generated_from_trainer #dataset-qmeeus/slue-voxpopuli #base_model-openai/whisper-small #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

WhisperForNamedEntityRecognition
================================


This model is a fine-tuned version of openai/whisper-small on the qmeeus/slue-voxpopuli dataset.
It achieves the following results on the evaluation set:


* Loss: 8.1514
* Wer: 10.4828


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: 0.0001
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 200
* training\_steps: 1600
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.26.0.dev0
* Pytorch 1.10.0
* Datasets 2.7.1.dev0
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 200
* training\_steps: 1600
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.0.dev0
* Pytorch 1.10.0
* Datasets 2.7.1.dev0
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #tensorboard #whisper_for_slu #token-classification #whisper-event #generated_from_trainer #dataset-qmeeus/slue-voxpopuli #base_model-openai/whisper-small #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 200
* training\_steps: 1600
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.0.dev0
* Pytorch 1.10.0
* Datasets 2.7.1.dev0
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #tensorboard #whisper_for_slu #token-classification #whisper-event #generated_from_trainer #dataset-qmeeus/slue-voxpopuli #base_model-openai/whisper-small #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 200
* training\_steps: 1600
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.26.0.dev0
* Pytorch 1.10.0
* Datasets 2.7.1.dev0
* Tokenizers 0.11.0