
# Statscoder

This model is a fine-tuned version of [bigcode/santacoder](https://huggingface.co/bigcode/santacoder) on `R` and `SAS` language repositories in [the stack ](https://huggingface.co/datasets/bigcode/the-stack-dedup) dataset.


## Training procedure

The model was finetuned using the code adapted from [loubnabnl/santacoder-finetuning](https://github.com/loubnabnl/santacoder-finetuning). Adapted to handle multiple subsets of datasets and it is [here](https://github.com/infinitylogesh/santacoder-finetuning).

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- optimizer: adafactor
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1600
- seq_length: 1024
- no_fp16

{"license": "openrail", "library_name": "transformers", "tags": ["code_generation", "R programming", "sas", "santacoder"], "datasets": ["bigcode/the-stack-dedup"]}
transformers pytorch gpt2 text-generation code_generation R programming sas santacoder custom_code dataset:bigcode/the-stack-dedup license:openrail autotrain_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #gpt2 #text-generation #code_generation #R programming #sas #santacoder #custom_code #dataset-bigcode/the-stack-dedup #license-openrail #autotrain_compatible #text-generation-inference #region-us 


# Statscoder

This model is a fine-tuned version of bigcode/santacoder on 'R' and 'SAS' language repositories in the stack  dataset.


## Training procedure

The model was finetuned using the code adapted from loubnabnl/santacoder-finetuning. Adapted to handle multiple subsets of datasets and it is here.

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- optimizer: adafactor
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1600
- seq_length: 1024
- no_fp16

# Statscoder

This model is a fine-tuned version of bigcode/santacoder on 'R' and 'SAS' language repositories in the stack  dataset. ## Training procedure

The model was finetuned using the code adapted from loubnabnl/santacoder-finetuning. Adapted to handle multiple subsets of datasets and it is here.

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- optimizer: adafactor
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1600
- seq_length: 1024
- no_fp16
TAGS
#transformers #pytorch #gpt2 #text-generation #code_generation #R programming #sas #santacoder #custom_code #dataset-bigcode/the-stack-dedup #license-openrail #autotrain_compatible #text-generation-inference #region-us 
 # Statscoder

This model is a fine-tuned version of bigcode/santacoder on 'R' and 'SAS' language repositories in the stack  dataset. ## Training procedure

The model was finetuned using the code adapted from loubnabnl/santacoder-finetuning. Adapted to handle multiple subsets of datasets and it is here.

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- optimizer: adafactor
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1600
- seq_length: 1024
- no_fp16
TAGS
#transformers #pytorch #gpt2 #text-generation #code_generation #R programming #sas #santacoder #custom_code #dataset-bigcode/the-stack-dedup #license-openrail #autotrain_compatible #text-generation-inference #region-us 
# Statscoder

This model is a fine-tuned version of bigcode/santacoder on 'R' and 'SAS' language repositories in the stack  dataset.## Training procedure

The model was finetuned using the code adapted from loubnabnl/santacoder-finetuning. Adapted to handle multiple subsets of datasets and it is here.

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- optimizer: adafactor
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1600
- seq_length: 1024
- no_fp16