| --- |
| library_name: setfit |
| tags: |
| - setfit |
| - sentence-transformers |
| - text-classification |
| - generated_from_setfit_trainer |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| - f1 |
| widget: |
| - text: '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?' |
| - text: 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? |
| - text: '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:' |
| - text: '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?' |
| - text: '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: true |
| 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 |
| - type: precision |
| value: 0.8248924988055423 |
| name: Precision |
| - type: recall |
| value: 0.81875 |
| name: Recall |
| - type: f1 |
| value: 0.8178892421209625 |
| name: F1 |
| --- |
| |
| # SetFit with flax-sentence-embeddings/stackoverflow_mpnet-base |
| |
| This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [flax-sentence-embeddings/stackoverflow_mpnet-base](https://huggingface.co/flax-sentence-embeddings/stackoverflow_mpnet-base) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. |
| |
| The model has been trained using an efficient few-shot learning technique that involves: |
| |
| 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. |
| 2. Training a classification head with features from the fine-tuned Sentence Transformer. |
| |
| ## Model Details |
| |
| ### Model Description |
| - **Model Type:** SetFit |
| - **Sentence Transformer body:** [flax-sentence-embeddings/stackoverflow_mpnet-base](https://huggingface.co/flax-sentence-embeddings/stackoverflow_mpnet-base) |
| - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance |
| - **Maximum Sequence Length:** 512 tokens |
| - **Number of Classes:** 2 classes |
| <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
| |
| ### Model Sources |
| |
| - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) |
| - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) |
| - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) |
| |
| ### Model Labels |
| | Label | Examples | |
| |:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | 1 | <ul><li>'In tf.gradients, there is a keyword argument grad_ys Why is grads_ys needed here? The docs here is implicit. Could you please give some specific purpose and code? And my example code for tf.gradients is'</li><li>'I am coding a Convolutional Neural Network to classify images in TensorFlow but there is a problem: When I try to feed my NumPy array of flattened images (3 channels with RGB values from 0 to 255) to a tf.estimator.inputs.numpy_input_fn I get the following error: My numpy_imput_fn looks like this: In the documentation for the function it is said that x should be a dict of NumPy array:'</li><li>'I am trying to use tf.pad. Here is my attempt to pad the tensor to length 20, with values 10. I get this error message I am looking at the documentation https://www.tensorflow.org/api_docs/python/tf/pad But I am unable to figure out how to shape the pad value'</li></ul> | |
| | 0 | <ul><li>"I am trying to use tf.train.shuffle_batch to consume batches of data from a TFRecord file using TensorFlow 1.0. The relevant functions are: The code enters through examine_batches(), having been handed the output of batch_generator(). batch_generator() calls tfrecord_to_graph_ops() and the problem is in that function, I believe. I am calling on a file with 1,000 bytes (numbers 0-9). If I call eval() on this in a Session, it shows me all 1,000 elements. But if I try to put it in a batch generator, it crashes. If I don't reshape targets, I get an error like ValueError: All shapes must be fully defined when tf.train.shuffle_batch is called. If I call targets.set_shape([1]), reminiscent of Google's CIFAR-10 example code, I get an error like Invalid argument: Shape mismatch in tuple component 0. Expected [1], got [1000] in tf.train.shuffle_batch. I also tried using tf.strided_slice to cut a chunk of the raw data - this doesn't crash but it results in just getting the first event over and over again. What is the right way to do this? To pull batches from a TFRecord file? Note, I could manually write a function that chopped up the raw byte data and did some sort of batching - especially easy if I am using the feed_dict approach to getting data into the graph - but I am trying to learn how to use TensorFlow's TFRecord files and how to use their built in batching functions. Thanks!"</li><li>"I am fairly new to TF and ML in general, so I have relied heavily on the documentation and tutorials provided by TF. I have been following along with the Tensorflow 2.0 Objection Detection API tutorial to the letter and have encountered an issue while training: everytime I run the training script model_main_tf2.py, it always hangs after the output: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2) after a number of depreciation warnings. I have tried many different ways of fixing this, including modifying the train script and pipeline.config files. My dataset isn't very large, less than 100 images with a max of 15 labels per image. useful info: Python 3.8.0 Tensorflow 2.4.4 (Non GPU) Windows 10 Pro Any and all help is appreciated!"</li><li>'I found two solutions to calculate FLOPS of Keras models (TF 2.x): [1] https://github.com/tensorflow/tensorflow/issues/32809#issuecomment-849439287 [2] https://github.com/tensorflow/tensorflow/issues/32809#issuecomment-841975359 At first glance, both seem to work perfectly when testing with tf.keras.applications.ResNet50(). The resulting FLOPS are identical and correspond to the FLOPS of the ResNet paper. But then I built a small GRU model and found different FLOPS for the two methods: This results in the following numbers: 13206 for method [1] and 18306 for method [2]. That is really confusing... Does anyone know how to correctly calculate FLOPS of recurrent Keras models in TF 2.x? EDIT I found another information: [3] https://github.com/tensorflow/tensorflow/issues/36391#issuecomment-596055100 When adding this argument to convert_variables_to_constants_v2, the outputs of [1] and [2] are the same when using my GRU example. The tensorflow documentation explains this argument as follows (https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/framework/convert_to_constants.py): Can someone try to explain this?'</li></ul> | |
| |
| ## Evaluation |
| |
| ### Metrics |
| | Label | Accuracy | Precision | Recall | F1 | |
| |:--------|:---------|:----------|:-------|:-------| |
| | **all** | 0.8187 | 0.8249 | 0.8187 | 0.8179 | |
| |
| ## Uses |
| |
| ### Direct Use for Inference |
| |
| First install the SetFit library: |
| |
| ```bash |
| pip install setfit |
| ``` |
| |
| Then you can load this model and run inference. |
| |
| ```python |
| from setfit import SetFitModel |
| |
| # Download from the 🤗 Hub |
| model = SetFitModel.from_pretrained("sharukat/so_mpnet-base_question_classifier") |
| # Run inference |
| preds = model("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?") |
| ``` |
| |
| <!-- |
| ### Downstream Use |
| |
| *List how someone could finetune this model on their own dataset.* |
| --> |
| |
| <!-- |
| ### Out-of-Scope Use |
| |
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
| |
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| ## Bias, Risks and Limitations |
| |
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
| |
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| ### Recommendations |
| |
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
| |
| ## Training Details |
| |
| ### Training Set Metrics |
| | Training set | Min | Median | Max | |
| |:-------------|:----|:---------|:----| |
| | Word count | 12 | 128.0219 | 907 | |
| |
| | Label | Training Sample Count | |
| |:------|:----------------------| |
| | 0 | 320 | |
| | 1 | 320 | |
| |
| ### Training Hyperparameters |
| - batch_size: (8, 8) |
| - num_epochs: (1, 16) |
| - max_steps: -1 |
| - sampling_strategy: unique |
| - body_learning_rate: (2e-05, 1e-05) |
| - head_learning_rate: 0.01 |
| - loss: CosineSimilarityLoss |
| - distance_metric: cosine_distance |
| - margin: 0.25 |
| - end_to_end: False |
| - use_amp: False |
| - warmup_proportion: 0.1 |
| - max_length: 256 |
| - seed: 42 |
| - eval_max_steps: -1 |
| - load_best_model_at_end: True |
|
|
| ### Training Results |
| | Epoch | Step | Training Loss | Validation Loss | |
| |:-------:|:---------:|:-------------:|:---------------:| |
| | 0.0000 | 1 | 0.3266 | - | |
| | **1.0** | **25640** | **0.0** | **0.2863** | |
|
|
| * The bold row denotes the saved checkpoint. |
| ### Framework Versions |
| - Python: 3.10.13 |
| - SetFit: 1.0.3 |
| - Sentence Transformers: 2.5.1 |
| - Transformers: 4.38.1 |
| - PyTorch: 2.1.2 |
| - Datasets: 2.18.0 |
| - Tokenizers: 0.15.2 |
|
|
| ## Citation |
|
|
| ### BibTeX |
| ```bibtex |
| @article{https://doi.org/10.48550/arxiv.2209.11055, |
| doi = {10.48550/ARXIV.2209.11055}, |
| url = {https://arxiv.org/abs/2209.11055}, |
| author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, |
| keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
| title = {Efficient Few-Shot Learning Without Prompts}, |
| publisher = {arXiv}, |
| year = {2022}, |
| copyright = {Creative Commons Attribution 4.0 International} |
| } |
| ``` |
|
|
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