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# LKD_Experience_all_labels

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an a dataset of Reddit comments and posts related to Living Kidney Donation (LKD).
The first word/token of each document is the name of the subreddit where the post was written. If an original post, the document is the title of the post, as well as the post description, where avaialble.
At present, the labels are unevenly distributed so the model does not perform well (to be remedied in the near future). 


It achieves the following results on the evaluation set:
- Loss: 1.0036
- Accuracy: 0.7429

## 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: 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 66   | 1.6537          | 0.3286   |
| No log        | 2.0   | 132  | 1.3005          | 0.7      |
| No log        | 3.0   | 198  | 1.1173          | 0.7429   |
| No log        | 4.0   | 264  | 1.0296          | 0.7429   |
| No log        | 5.0   | 330  | 1.0036          | 0.7429   |


### Framework versions

- Transformers 4.27.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "widget": [{"text": "Showerthoughts Donate 1 kidney and you are a hero donate 100 and you become villain.", "example_title": "Noise"}, {"text": "IAmA Eight years ago today I donated a kidney to a friend. AMA", "example_title": "Past Direct"}, {"text": "transplant Donated a kidney 4 days ago at UVA. So far so great. Not exactly an AMA, but I'm happy to answer some questions about the experience.  Did the procedure at UVA.  Dr. Jos\u00c3\u00a9 Oberholzer took out my left kidney using a da Vinci surgical robot.  Anesthesiologist gave me Exparel, which is a slow release nerve block which was amazing.  Surgery was in the morning; was up and walking around by that afternoon.  I have just been taking Tylenol every 6 hours since coming home.  So far, zero pain.  My dad got my kidney and he immediately started feeling better.  He's in the ER as we speak because he hasn't pooped in a week, but they say that's pretty normal.", "example_title": "Current Direct"}, {"text": "Assistance A childhood friend of mine is in the last stages of kidney disease and needs to find a rare type of donor that matches. He's been married for just a few years and has 3 kids. Is there anything that anybody can do to help spread the word? (info page at the link) I don't normally do this type of thing, but he's a childhood friend, volunteer firefighter and has a young family. He's has news coverage, but has had no luck yet.  His doctor says that he's almost out of time due to his inability to take regular dialysis due to complications.", "example_title": "Current Indirect"}, {"text": "medicine A Maryland man who died from rabies after receiving a kidney from an infected donor is the eighth known victim of rabies-tainted solid-organ transplants.", "example_title": "News"}, {"text": "AskReddit [Serious] Reddit with dialysis costing Governments billions of dollars and live kidney donations so incredibly low (9.4 donors per 10000 in Australia) would you donate a kidney for financial compensation and if so how much would you do it for?", "example_title": "Hypothetical/Impersonal"}], "model-index": [{"name": "LKD_Experience_all_labels", "results": []}]}
transformers pytorch tensorboard distilbert text-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

LKD\_Experience\_all\_labels
============================


This model is a fine-tuned version of distilbert-base-uncased on an a dataset of Reddit comments and posts related to Living Kidney Donation (LKD).
The first word/token of each document is the name of the subreddit where the post was written. If an original post, the document is the title of the post, as well as the post description, where avaialble.
At present, the labels are unevenly distributed so the model does not perform well (to be remedied in the near future).


It achieves the following results on the evaluation set:


* Loss: 1.0036
* Accuracy: 0.7429


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: 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: 5


### Training results



### Framework versions


* Transformers 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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: 5 ### Training results ### Framework versions


* Transformers 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #distilbert #text-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: 1e-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: 5 ### Training results ### Framework versions


* Transformers 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #distilbert #text-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: 1e-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: 5### Training results### Framework versions


* Transformers 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2