--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: flan-t5-xl-finetuned-unnatural-instructions results: [] widget: - text: "You will be provided with a short text that you should read. After reading the text, answer the question 'Who is telling the story in first person?'\nText: I went to the store and bought some milk.\nThe output should be one of the two options: 'I' or 'Not I'.\nOutput:" - text: "You are given the title and opening paragraph of an article. Your task is to find the main idea of the article using context clues from the text.\nText: The first confirmed case of Zika in Africa has been found in Uganda, health officials said on Tuesday, more than 4,000 miles away from Brazil where a major outbreak began last year.\nAblood test carried out at a private hospital showed that a five-year-old girl who had returned from Mozambique three weeks ago had contracted Zika, which can cause birth defects in babies born to infected mothers.\nOutput:" - text: "We have given you a list of sentences. Your task is to go through this list and find all the unique words used in these sentences and store them in a dictionary.\nThis is an example sentence., That is another example sentence., Here is an interesting sentence!\nThe output should be a Python dictionary with keys as the unique words and values as the number of times that word occurs in the text.\nOutput:\n" - text: "You will given a set of two or more words. Output the shortest word in the set. If there is a tie for the shortest word, output all tied words in alphabetical order, separated by space.\nWords: defenestrate, circumambulate, excommunication.\nThe input will be lowercase and only contain alphanumeric characters and spaces.\nOutput:\n" inference: parameters: max_length: 150 --- # flan-t5-xl-finetuned-unnatural-instructions This model is a fine-tuned version of [google/flan-t5-xl](https://huggingface.co/google/flan-t5-xl) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1224 ## 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: 5e-05 - train_batch_size: 1 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 5000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.1345 | 0.02 | 1000 | 0.1386 | | 0.14 | 0.03 | 2000 | 0.1336 | | 0.1402 | 0.05 | 3000 | 0.1282 | | 0.129 | 0.07 | 4000 | 0.1235 | | 0.1356 | 0.08 | 5000 | 0.1224 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2