Finetune gpt2 full#

However, adapters have several advantages over fully finetuning, e.g., shorter training times, they require less memory to be stored, and they can easily be shared.

Finetune gpt2 full#

We observe that the models with adapters achieve a competitive results to full model fine-tuning. The results are given in the following table: We trained the GPT-2 model with small-sized GPT-2 vocabulary using maximum likelihood estimation. It is not sufficient for the model to simply enter a number from the table it needs to count all countries the album was released in, in 2008. Gold sentence: Black Ice was released in 6 Countries in 2008. For example, given a table containing the release dates for an album, the model is provided with a template and and has the objective to fill in the blanks. This task requires the model to learn to generate entailing sentences w.r.t. We train a GPT-2 model on the task proposed by Chen et al. Now we will take a look at the scores for sequence-to-sequence tasks. In general, we can use adapters instead of fully finetuning the model without a deterioration in downstream task performance. This matches the results of other models with adapters. On some tasks such as SST-2, the adapters achieve a higher score than the fully finetuned model for GPT-2 and BART. The results of the adapters are comparable to those of the fully finetuned model. The adapters are trained with early stopping for a maximum of 15 epochs with a learning rate of 1e-4. The fully-finetuned BART model is trained for 3 epochs with a learning rate of 4e-5. The adapters are trained for 10 epochs with a learning rate of 1e-4. The fully finetuned GPT-2 model is trained for 4 epochs with a learning rate of 1e-4. The GPT-2 model and BART models achieve the following scores: GPT-2 We compare the scores of a fully finetuned model with the scores of adapter-based models, either using the adapter configuration of Pfeiffer et al. Results of BART and GPT-2 with adaptersīefore we dive into generation tasks, we will take a look at the performance on the GLUE benchmark. This will allow researchers and engineers to use adapters for sequence-to-sequence tasks. In the new version 2.0, the framework now also provides adapters for the language generation models BART and GPT-2. Up until now, the framework included adapters for the models BERT, RoBERTa, XML-RoBERTa and DistilBERT.

The AdapterHub framework makes adapters easy to use. The modularity aspect of adapters in zero-shot machine translation has recently been demonstrated by Philip et al.

On a neural machine translation task, they achieved similar results with adapters as compared to a fully finetuned model.

(2019) have shown that adapters are useful for sequence to sequence tasks. As adapters are encapsulated between frozen layers, they can be regarded as modular units which can be composed in a number of different ways (For more details and examples check out this blog post). Adapters also enable new possibilities in transfer learning. Furthermore, the lower number of parameters requires less memory and makes it easier to share the trained adapters. As a result, it is sufficient to only store the adapter layers instead of storing fully finetuned models separately for each task. During training, only the parameters of the adapter layers are finetuned, while the parameters of the pre-trained model remain frozen. Adapters are small layers that are stitched into pre-trained transformer-based models. For instance, they enable us to efficiently train and share new task-specific models. Adapters are becoming more and more important in machine learning for NLP.