To increase detection sensitivity, photoactivatable-ribonucleoside-enhanced CLIP (PAR-CLIP) was also developed. For example, high-throughput sequencing of RNA isolated by cross-linking immunoprecipitation (HITS-CLIP) was used to identify approximately 30 to 60 nucleotide regions around the peaks of CLIP read clusters that represent binding sites of RNA-binding proteins (RBPs). Recent technological developments, especially the technique of crosslinking immunoprecipitation coupled with high-throughput sequencing (CLIP-seq), have provided powerful tools for studying the roles of RNA regulation in the control of gene expression and the generation of phenotypic complexity. TODO: add more further reading.RNA’s diversity in sequence and structure endows it with crucial roles in cell biology. Further Readingįor more information about GPT-CC, GitHub Copilot, etc, see: TODO: more information about this when complete. We also have Huggingface's Space demo where you can specify and problem in the format of a programming competition question.
CLIPY ONLINE CODE
Human Eval Results Eval ResultsĪ Visual Studio Code which uses the HuggingFace Inference API is available and can be found here. The models are also evaluated on the APPS and HumanEval datasets. TODO: which is the recommended way to train GPT-CC? Evaluation We trained model for 5 epochs selecting best checkpoint judging by validation loss. The choice of hyperparameters for 1.3B model is in part determined by hardware limitations. The language modelling objective for APPS dataset is modified to backpropagate loss only for the tokens corresponding to code solution (refer to Hendrycks et al for more details).įor fine-tuning GPTNe0-1.3B on APPS dataset we used Adafactor optimizer with linear learning rate schedule (5k warmup steps from 0 to 2e-5 followed by linear decay to 0), weight decay 0.1 and batch size 24, sequence length 1024. The choice of relatively large batch size and low LR with long warmup are made to avoid agressive updates and preserve the knowledge contained in pretrained GPTNeo weights.įor fine-tuning GPTNe0-125M on APPS dataset we used AdamW optimizer (beta1=0.9, beta2=0.98) with linear learning rate schedule (800 warmup steps from 0 to peak LR followed by linear decay to 0, a range of value for peak LR was ), weight decay 0.1 and batch size 256, sequence length 1024.
Training is done using the training scripts available here.įor fine-tuning GPTNeo-125M on CodeClippy dataset we used AdamW optimizer (beta1=0.9, beta2=0.95) with GPT3-like learning rate schedule (4k warmup steps from 0 to 5e-5 followed by 50k cosine decay steps to 5e-6), weight decay 0.1 and batch size 1024, sequence length 2048. TODO: which is the recommended model? Training
The ones that perform relatively well (None improve on the standard GPT-Neo 125M model except for APPs specific models and only for the APPs task): The GPT-CC models are fine-tuned versions of GPT-2 and GPT-Neo. One intermittent fix would be to use tools like lib-magic to some extension for the purpose of filtering.
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