YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

This is a fine-tuned version of T5 FLAN LARGE (783M) on English in particular on the public dataset spider for text-toSQL.

To initialize the model:

from transformers import T5ForConditionalGeneration
model = T5ForConditionalGeneration.from_pretrained("MRNH/flan-t5-large-PLsql")

Use the tokenizer:

tokenizer = T5ForConditionalGeneration.from_pretrained("MRNH/flan-t5-large-PLsql")

input = tokenizer("<question> "+sentence["db_id"]+" </question> "+sentence["question"],
                  text_target=sentence["query"], return_tensors='pt')

To generate text using the model:

output = model.generate(input["input_ids"],attention_mask=input["attention_mask"])

Training of the model is performed using the following loss computation based on the hidden state output h:

h.logits, h.loss = model(input_ids=input["input_ids"],
                                              attention_mask=input["attention_mask"],
                                              labels=input["labels"])

Downloads last month: 20