Model: sharukat/so_mpnet-base_question_classifier
Author: sharukat
Pipeline: text-classification
Library: setfit
Tags: setfit, safetensors, mpnet, sentence-transformers, text-classification, generated_from_setfit_trainer, arxiv:2209.11055, base_model:flax-sentence-embeddings/stackoverflow_mpnet-base, base_model:finetune:flax-sentence-embeddings/stackoverflow_mpnet-base, model-index, region:us
Base models (finetune): id: flax-sentence-embeddings/stackoverflow_mpnet-base
Card metadata: library_name: setfit
tags: setfit, sentence-transformers, text-classification, generated_from_setfit_trainer
metrics: accuracy, precision, recall, f1
widget: I'm trying to take a dataframe and convert them to tensors to train a model in keras. I think it's being triggered when I am converting my Y label to a tensor: I'm getting the following error when casting y_train to tensor from slices: In the tutorials this seems to work but I think those tutorials are doing multiclass classifications whereas I'm doing a regression so y_train is a series not multiple columns. Any suggestions of what I can do?, My weights are defined as I want to use the weights decay so I add, for example, the argument to the tf.get_variable. Now I'm wondering if during the evaluation phase this is still correct or maybe I have to set the regularizer factor to 0. There is also another argument trainable. The documentation says If True also add the variable to the graph collection GraphKeys.TRAINABLE_VARIABLES. which is not clear to me. Should I use it? Can someone explain to me if the weights decay effects in a sort of wrong way the evaluation step? How can I solve in that case?, Maybe I'm confused about what "inner" and "outer" tensor dimensions are, but the documentation for tf.matmul puzzles me: Isn't it the case that R-rank arguments need to have matching (or no) R-2 outer dimensions, and that (as in normal matrix multiplication) the Rth, inner dimension of the first argument must match the R-1st dimension of the second. That is, in The outer dimensions a, ..., z must be identical to a', ..., z' (or not exist), and x and x' must match (while p and q can be anything). Or put another way, shouldn't the docs say:, I am using tf.data with reinitializable iterator to handle training and dev set data. For each epoch, I initialize the training data set. The official documentation has similar structure. I think this is not efficient especially if the training set is large. Some of the resources I found online has sess.run(train_init_op, feed_dict={X: X_train, Y: Y_train}) before the for loop to avoid this issue. But then we can't process the dev set after each epoch; we can only process it after we are done iterating over epochs epochs. Is there a way to efficiently process the dev set after each epoch?, Why is the pred variable being calculated before any of the training iterations occur? I would expect that a pred would be generated (through the RNN() function) during each pass through of the data for every iteration? There must be something I am missing. Is pred something like a function object? I have looked at the docs for tf.matmul() and that returns a tensor, not a function. Full source: https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/recurrent_network.py Here is the code:
pipeline_tag: text-classification
inference: yes
base_model: flax-sentence-embeddings/stackoverflow_mpnet-base
model-index: name: SetFit with flax-sentence-embeddings/stackoverflow_mpnet-base
results: task: type: text-classification
name: Text Classification
dataset: name: Unknown
type: unknown
split: test
metrics: type: accuracy
value: 0.81875
name: Accuracy
verified: no, type: precision
value: 0.8248924988055423
name: Precision
verified: no, type: recall
value: 0.81875
name: Recall
verified: no, type: f1
value: 0.8178892421209625
name: F1
verified: no
Likes: 0 | All-time downloads: 2124
Created: 2024-03-03 05:22:06