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# Clinical trial stop reasons

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the task of classification of why a clinical trial has stopped early.

The dataset containing 3,747 manually curated reasons used for fine-tuning is available in the [Hub](https://huggingface.co/datasets/opentargets/clinical_trial_reason_to_stop).

More details on the model training are available in the GitHub project ([link](https://github.com/opentargets/stopReasons)) and in the associated publication (TBC).


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-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: 7

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy Thresh |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|
| No log        | 1.0   | 106  | 0.1824          | 0.9475          |
| No log        | 2.0   | 212  | 0.1339          | 0.9630          |
| No log        | 3.0   | 318  | 0.1109          | 0.9689          |
| No log        | 4.0   | 424  | 0.0988          | 0.9741          |
| 0.1439        | 5.0   | 530  | 0.0943          | 0.9743          |
| 0.1439        | 6.0   | 636  | 0.0891          | 0.9763          |
| 0.1439        | 7.0   | 742  | 0.0899          | 0.9760          |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.12.1+cu102
- Datasets 2.9.0
- Tokenizers 0.13.2
{"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["generated_from_trainer", "medical"], "datasets": ["opentargets/clinical_trial_reason_to_stop"], "metrics": ["accuracy"], "widget": [{"text": "Study stopped due to problems to recruit patients", "example_title": "Enrollment issues"}, {"text": "Efficacy endpoint unmet", "example_title": "Negative reasons"}, {"text": "Study stopped due to unexpected adverse effects", "example_title": "Safety"}, {"text": "Study paused due to the pandemic", "example_title": "COVID-19"}], "base_model": "bert-base-uncased", "model-index": [{"name": "stop_reasons_classificator_multilabel", "results": []}]}
transformers pytorch tensorboard safetensors bert text-classification generated_from_trainer medical en dataset:opentargets/clinical_trial_reason_to_stop base_model:bert-base-uncased license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #medical #en #dataset-opentargets/clinical_trial_reason_to_stop #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

Clinical trial stop reasons
===========================


This model is a fine-tuned version of bert-base-uncased on the task of classification of why a clinical trial has stopped early.


The dataset containing 3,747 manually curated reasons used for fine-tuning is available in the Hub.


More details on the model training are available in the GitHub project (link) and in the associated publication (TBC).


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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: 7


### Training results



### Framework versions


* Transformers 4.26.0
* Pytorch 1.12.1+cu102
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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: 7 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.12.1+cu102
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #medical #en #dataset-opentargets/clinical_trial_reason_to_stop #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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: 7 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.12.1+cu102
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #medical #en #dataset-opentargets/clinical_trial_reason_to_stop #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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: 7### Training results### Framework versions


* Transformers 4.26.0
* Pytorch 1.12.1+cu102
* Datasets 2.9.0
* Tokenizers 0.13.2