Ramyashree commited on
Commit
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Add SetFit model

Browse files
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README.md ADDED
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+ ---
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+ library_name: setfit
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+ tags:
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+ - setfit
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+ - sentence-transformers
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+ - text-classification
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+ - generated_from_setfit_trainer
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+ datasets:
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+ - Ramyashree/Dataset-train-test-withlabel-text
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+ metrics:
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+ - accuracy
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+ widget:
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+ - text: I weant to use my other account, switch them
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+ - text: I can't remember my password, help me reset it
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+ - text: the game was postponed and i wanna get a reimbursement
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+ - text: where to change to another online account
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+ - text: the show was cancelled, get a reimbursement
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+ pipeline_tag: text-classification
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+ inference: true
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+ base_model: thenlper/gte-large
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+ model-index:
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+ - name: SetFit with thenlper/gte-large
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+ results:
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+ - task:
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+ type: text-classification
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+ name: Text Classification
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+ dataset:
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+ name: Ramyashree/Dataset-train-test-withlabel-text
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+ type: Ramyashree/Dataset-train-test-withlabel-text
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+ split: test
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+ metrics:
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+ - type: accuracy
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+ value: 0.1
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+ name: Accuracy
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+ ---
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+
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+ # SetFit with thenlper/gte-large
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+
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+ This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [Ramyashree/Dataset-train-test-withlabel-text](https://huggingface.co/datasets/Ramyashree/Dataset-train-test-withlabel-text) dataset that can be used for Text Classification. This SetFit model uses [thenlper/gte-large](https://huggingface.co/thenlper/gte-large) 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.
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+
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+ The model has been trained using an efficient few-shot learning technique that involves:
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+
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+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** SetFit
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+ - **Sentence Transformer body:** [thenlper/gte-large](https://huggingface.co/thenlper/gte-large)
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+ - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
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+ - **Maximum Sequence Length:** 512 tokens
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+ - **Number of Classes:** 10 classes
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+ - **Training Dataset:** [Ramyashree/Dataset-train-test-withlabel-text](https://huggingface.co/datasets/Ramyashree/Dataset-train-test-withlabel-text)
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+ <!-- - **Language:** Unknown -->
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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+ - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
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+ - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
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+ - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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+
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+ ### Model Labels
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+ | Label | Examples |
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+ |:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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+ | 0 | <ul><li>"I don't have an online account, what do I have to do to register?"</li><li>'can you tell me if i can regisger two accounts with a single email address?'</li><li>'I have no online account, open one, please'</li></ul> |
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+ | 1 | <ul><li>'how can I modify the information on my profile?'</li><li>'can u ask an agent how to make changes to my profile?'</li><li>'I want to update the information on my profile'</li></ul> |
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+ | 2 | <ul><li>'can I close my account?'</li><li>"I don't want my account, can you delete it?"</li><li>'how do i close my online account?'</li></ul> |
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+ | 3 | <ul><li>'I would like to use my other online account , could you switch them, please?'</li><li>'i want to use my other online account, can u change them?'</li><li>'how do i change to another account?'</li></ul> |
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+ | 4 | <ul><li>'what can you tell me about getting some bills?'</li><li>'tell me where I can request a bill'</li><li>'ask an agent if i can obtain some bills'</li></ul> |
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+ | 5 | <ul><li>'the game was postponed, help me obtain a reimbursement'</li><li>'the game was postponed, what should I do to obtain a reimbursement?'</li><li>'the concert was postponed, what should I do to request a reimbursement?'</li></ul> |
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+ | 6 | <ul><li>'i have an issue making a payment with card and i want to inform of it, please'</li><li>'I got an error message when I attempted to pay, but my card was charged anyway and I want to notify it'</li><li>'I want to notify a problem making a payment, can you help me?'</li></ul> |
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+ | 7 | <ul><li>"I'm interested in your reimbursement polivy"</li><li>'i wanna see your refund policy, can u help me?'</li><li>'where do I see your money back policy?'</li></ul> |
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+ | 8 | <ul><li>'my online account was hacked and I want tyo get it back'</li><li>"I lost my password and I'd like to retrieve it, please"</li><li>'could u ask an agent how i can reset my password?'</li></ul> |
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+ | 9 | <ul><li>'tell me if my refund was processed'</li><li>'I need help checking the status of my refund'</li><li>'I want to see the status of my refund, can you help me?'</li></ul> |
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+
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+ ## Evaluation
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+
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+ ### Metrics
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+ | Label | Accuracy |
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+ |:--------|:---------|
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+ | **all** | 0.1 |
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+
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+ ## Uses
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+
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+ ### Direct Use for Inference
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+
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+ First install the SetFit library:
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+
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+ ```bash
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+ pip install setfit
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+ ```
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+
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+ Then you can load this model and run inference.
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+
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+ ```python
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+ from setfit import SetFitModel
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+
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+ # Download from the 🤗 Hub
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+ model = SetFitModel.from_pretrained("Ramyashree/gte-large-train-test-4")
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+ # Run inference
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+ preds = model("where to change to another online account")
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+ ```
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+
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+ <!--
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+ ### Downstream Use
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+
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+ *List how someone could finetune this model on their own dataset.*
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+ -->
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+
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+ <!--
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+ ### Out-of-Scope Use
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+
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+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
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+ -->
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+
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+ <!--
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+ ## Bias, Risks and Limitations
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+
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+ *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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+ -->
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+
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+ <!--
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+ ### Recommendations
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+
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+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
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+
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+ ## Training Details
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+
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+ ### Training Set Metrics
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+ | Training set | Min | Median | Max |
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+ |:-------------|:----|:-------|:----|
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+ | Word count | 3 | 10.258 | 24 |
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+
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+ | Label | Training Sample Count |
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+ |:------|:----------------------|
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+ | 0 | 50 |
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+ | 1 | 50 |
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+ | 2 | 50 |
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+ | 3 | 50 |
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+ | 4 | 50 |
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+ | 5 | 50 |
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+ | 6 | 50 |
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+ | 7 | 50 |
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+ | 8 | 50 |
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+ | 9 | 50 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (16, 16)
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+ - num_epochs: (1, 1)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - num_iterations: 20
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+ - body_learning_rate: (2e-05, 1e-05)
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+ - head_learning_rate: 0.01
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+ - loss: CosineSimilarityLoss
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+ - distance_metric: cosine_distance
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+ - margin: 0.25
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+ - end_to_end: False
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+ - use_amp: False
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+ - warmup_proportion: 0.1
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+ - seed: 42
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+ - eval_max_steps: -1
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+ - load_best_model_at_end: False
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+
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+ ### Training Results
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+ | Epoch | Step | Training Loss | Validation Loss |
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+ |:------:|:----:|:-------------:|:---------------:|
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+ | 0.0008 | 1 | 0.3248 | - |
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+ | 0.04 | 50 | 0.1606 | - |
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+ | 0.08 | 100 | 0.0058 | - |
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+ | 0.12 | 150 | 0.0047 | - |
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+ | 0.16 | 200 | 0.0009 | - |
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+ | 0.2 | 250 | 0.0007 | - |
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+ | 0.24 | 300 | 0.001 | - |
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+ | 0.28 | 350 | 0.0008 | - |
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+ | 0.32 | 400 | 0.0005 | - |
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+ | 0.36 | 450 | 0.0004 | - |
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+ | 0.4 | 500 | 0.0005 | - |
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+ | 0.44 | 550 | 0.0005 | - |
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+ | 0.48 | 600 | 0.0006 | - |
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+ | 0.52 | 650 | 0.0005 | - |
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+ | 0.56 | 700 | 0.0004 | - |
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+ | 0.6 | 750 | 0.0004 | - |
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+ | 0.64 | 800 | 0.0002 | - |
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+ | 0.68 | 850 | 0.0003 | - |
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+ | 0.72 | 900 | 0.0002 | - |
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+ | 0.76 | 950 | 0.0002 | - |
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+ | 0.8 | 1000 | 0.0003 | - |
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+ | 0.84 | 1050 | 0.0002 | - |
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+ | 0.88 | 1100 | 0.0002 | - |
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+ | 0.92 | 1150 | 0.0003 | - |
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+ | 0.96 | 1200 | 0.0003 | - |
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+ | 1.0 | 1250 | 0.0003 | - |
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+
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+ ### Framework Versions
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+ - Python: 3.10.12
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+ - SetFit: 1.0.1
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+ - Sentence Transformers: 2.2.2
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+ - Transformers: 4.35.2
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+ - PyTorch: 2.1.0+cu121
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+ - Datasets: 2.15.0
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+ - Tokenizers: 0.15.0
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+
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+ ## Citation
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+
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+ ### BibTeX
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+ ```bibtex
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+ @article{https://doi.org/10.48550/arxiv.2209.11055,
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+ doi = {10.48550/ARXIV.2209.11055},
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+ url = {https://arxiv.org/abs/2209.11055},
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+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
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+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
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+ title = {Efficient Few-Shot Learning Without Prompts},
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+ publisher = {arXiv},
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+ year = {2022},
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+ copyright = {Creative Commons Attribution 4.0 International}
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+ }
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+ ```
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+
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+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
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+ -->
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+
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+ <!--
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+ ## Model Card Authors
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+
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+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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+ -->
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+
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+ <!--
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+ ## Model Card Contact
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+
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+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
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