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jamiehudson
/
725_model_v4

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
Model card Files Community
jamiehudson commited on
Commit
8951629
•
1 Parent(s): 1fffcb6

Push model using huggingface_hub.

Browse files
Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +1084 -0
  3. config.json +31 -0
  4. config_sentence_transformers.json +9 -0
  5. config_setfit.json +8 -0
  6. model.safetensors +3 -0
  7. model_head.pkl +3 -0
  8. modules.json +20 -0
  9. sentence_bert_config.json +4 -0
  10. special_tokens_map.json +37 -0
  11. tokenizer.json +0 -0
  12. tokenizer_config.json +57 -0
  13. vocab.txt +0 -0
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 384,
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+ "pooling_mode_cls_token": true,
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+ "pooling_mode_mean_tokens": false,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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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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+ metrics:
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+ - accuracy
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+ - f1
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+ - precision
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+ - recall
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+ widget:
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+ - text: man, product/whatever is my new best friend. i like product but the integration
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+ of product into office and product is a lot of fun. i just spent the day feeding
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+ it my training presentation i'm preparing in my day job and it was very helpful.
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+ almost better than humans.
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+ - text: that's great news! product is the perfect platform to share these advanced
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+ product prompts and help more users get the most out of it!
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+ - text: after only one week's trial of the new product with brand enabled, i have
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+ replaced my default browser product that i was using for more than 7 years with
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+ new product. i no longer need to spend a lot of time finding answers from a bunch
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+ of search results and web pages. it's amazing
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+ - text: very impressive. brand is finally fighting back. i am just a little worried
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+ about the scalability of such a high context window size, since even in their
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+ demos it took quite a while to process everything. regardless, i am very interested
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+ in seeing what types of capabilities a >1m token size window can unleash.
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+ - text: product the way it shows the sources is so fucking cool, this new ai is amazing
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+ pipeline_tag: text-classification
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+ inference: true
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+ base_model: BAAI/bge-small-en-v1.5
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+ model-index:
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+ - name: SetFit with BAAI/bge-small-en-v1.5
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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: Unknown
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+ type: unknown
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+ split: test
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+ metrics:
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+ - type: accuracy
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+ value: 0.964
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+ name: Accuracy
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+ - type: f1
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+ value:
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+ - 0.9130434782608695
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+ - 0.888888888888889
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+ - 0.9779951100244498
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+ name: F1
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+ - type: precision
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+ value:
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+ - 0.9545454545454546
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+ - 1.0
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+ - 0.9615384615384616
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+ name: Precision
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+ - type: recall
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+ value:
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+ - 0.875
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+ - 0.8
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+ - 0.9950248756218906
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+ name: Recall
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+ ---
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+
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+ # SetFit with BAAI/bge-small-en-v1.5
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+
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+ This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) 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:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)
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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:** 3 classes
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+ <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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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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+ | neither | <ul><li>'i asked brand to write it and then let it translate back. so in reality i have no clue what i am sending...'</li><li>"i saw someone summarize brand the other day; it doesn't give answers, it gives answer-shaped responses."</li><li>'thank you comrade i mean colleague. i will have brand summarize.'</li></ul> |
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+ | peak | <ul><li>'brand!! it helped me finish my resume. i just asked it if it could write my resume based on horribly written descriptions i came up with. and it made it all pretty:)'</li><li>'been building products for a bit now and your product (audio pen) is simple, useful and just works (like the early magic when product came out). congratulations and keep the flag flying high. not surprised that india is producing apps like yours. high time:-)'</li><li>'just got access to personalization in brand!! totally unexpected. very happy'</li></ul> |
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+ | pit | <ul><li>'brand recently i came across a very unwell patient in a psychiatric unit who was using product & this was reinforcing his delusional state & detrimentally impacting his mental health. anyone looking into this type of usage of product? what safe guards are being put in place?'</li><li>'brand product is def better at extracting numbers from images, product failed (pro version) twice...'</li><li>"the stuff brand gives is entirely too scripted *and* impractical, which is what i'm trying to avoid:/"</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 | F1 | Precision | Recall |
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+ |:--------|:---------|:------------------------------------------------------------|:----------------------------------------------|:---------------------------------|
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+ | **all** | 0.964 | [0.9130434782608695, 0.888888888888889, 0.9779951100244498] | [0.9545454545454546, 1.0, 0.9615384615384616] | [0.875, 0.8, 0.9950248756218906] |
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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("jamiehudson/725_model_v4")
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+ # Run inference
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+ preds = model("product the way it shows the sources is so fucking cool, this new ai is amazing")
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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 | 31.6606 | 98 |
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+
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+ | Label | Training Sample Count |
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+ |:--------|:----------------------|
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+ | pit | 277 |
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+ | peak | 265 |
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+ | neither | 1105 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (32, 32)
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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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+ - 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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+ | 0.0000 | 1 | 0.2683 | - |
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+ | 0.0012 | 50 | 0.2643 | - |
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+ | 0.0023 | 100 | 0.2432 | - |
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+ | 0.0035 | 150 | 0.2623 | - |
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+ | 0.0047 | 200 | 0.2527 | - |
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+ | 0.0058 | 250 | 0.2252 | - |
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+ | 0.0070 | 300 | 0.2362 | - |
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+ | 0.0082 | 350 | 0.2334 | - |
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+ | 0.0093 | 400 | 0.2189 | - |
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+ | 0.0105 | 450 | 0.2144 | - |
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+ | 0.0117 | 500 | 0.1971 | - |
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+ | 0.0129 | 550 | 0.1565 | - |
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+ | 0.0140 | 600 | 0.0816 | - |
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+ | 0.0152 | 650 | 0.1417 | - |
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+ | 0.0164 | 700 | 0.1051 | - |
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+ | 0.0175 | 750 | 0.0686 | - |
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+ | 0.0187 | 800 | 0.0394 | - |
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+ | 0.0199 | 850 | 0.0947 | - |
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+ | 0.0210 | 900 | 0.0468 | - |
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+ | 0.0222 | 950 | 0.0143 | - |
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+ | 0.0234 | 1000 | 0.0281 | - |
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+ | 0.0245 | 1050 | 0.0329 | - |
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+ | 0.0257 | 1100 | 0.0206 | - |
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+ | 0.0269 | 1150 | 0.0113 | - |
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+ | 0.0280 | 1200 | 0.0054 | - |
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+ | 0.0292 | 1250 | 0.0056 | - |
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+ | 0.0304 | 1300 | 0.0209 | - |
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+ | 0.0315 | 1350 | 0.0064 | - |
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+ | 0.0327 | 1400 | 0.0085 | - |
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+ | 0.0339 | 1450 | 0.0025 | - |
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+ | 0.0350 | 1500 | 0.0031 | - |
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+ | 0.0362 | 1550 | 0.0024 | - |
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+ | 0.0374 | 1600 | 0.0014 | - |
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+ | 0.0386 | 1650 | 0.0019 | - |
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+ | 0.0397 | 1700 | 0.0023 | - |
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+ | 0.0409 | 1750 | 0.0014 | - |
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+ | 0.0421 | 1800 | 0.002 | - |
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+ | 0.0432 | 1850 | 0.001 | - |
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+ | 0.0444 | 1900 | 0.001 | - |
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+ | 0.0456 | 1950 | 0.0019 | - |
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+ | 0.0467 | 2000 | 0.0017 | - |
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+ | 0.0479 | 2050 | 0.001 | - |
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+ | 0.0491 | 2100 | 0.0008 | - |
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+ | 0.0502 | 2150 | 0.0011 | - |
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+ | 0.0514 | 2200 | 0.0006 | - |
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+ | 0.0526 | 2250 | 0.0012 | - |
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+ | 0.0537 | 2300 | 0.0008 | - |
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+ | 0.0549 | 2350 | 0.0014 | - |
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+ | 0.0561 | 2400 | 0.0009 | - |
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+ | 0.0572 | 2450 | 0.0009 | - |
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+ | 0.0584 | 2500 | 0.001 | - |
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+ | 0.0631 | 2700 | 0.0004 | - |
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+ | 0.0643 | 2750 | 0.0007 | - |
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+ | 0.0654 | 2800 | 0.0005 | - |
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+ | 0.0666 | 2850 | 0.0007 | - |
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+ | 0.0678 | 2900 | 0.0007 | - |
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+ | 0.0689 | 2950 | 0.0006 | - |
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+ | 0.0701 | 3000 | 0.0005 | - |
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+ | 0.0876 | 3750 | 0.0039 | - |
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+ | 0.0888 | 3800 | 0.0004 | - |
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+ | 0.0900 | 3850 | 0.0003 | - |
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+ | 0.0911 | 3900 | 0.0004 | - |
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+ | 0.0923 | 3950 | 0.0007 | - |
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+ | 0.0935 | 4000 | 0.0003 | - |
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+ | 0.0946 | 4050 | 0.0004 | - |
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+ | 0.0958 | 4100 | 0.0003 | - |
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+ | 0.0970 | 4150 | 0.0003 | - |
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+ | 0.0981 | 4200 | 0.0004 | - |
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+ | 0.0993 | 4250 | 0.0003 | - |
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+ | 0.1005 | 4300 | 0.0004 | - |
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+ | 0.1016 | 4350 | 0.0003 | - |
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+ | 0.1028 | 4400 | 0.0004 | - |
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+ | 0.1063 | 4550 | 0.0003 | - |
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+ | 0.1110 | 4750 | 0.0016 | - |
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+ | 0.1145 | 4900 | 0.0003 | - |
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+ | 0.1168 | 5000 | 0.0003 | - |
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+ | 0.1180 | 5050 | 0.0003 | - |
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+ | 0.1192 | 5100 | 0.0003 | - |
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+ | 0.1203 | 5150 | 0.0002 | - |
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+ | 0.1215 | 5200 | 0.0003 | - |
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+ | 0.1227 | 5250 | 0.0002 | - |
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+ | 0.1238 | 5300 | 0.0178 | - |
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+ | 0.1250 | 5350 | 0.0014 | - |
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+ | 0.1262 | 5400 | 0.002 | - |
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+ | 0.1273 | 5450 | 0.0002 | - |
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+ | 0.1285 | 5500 | 0.0008 | - |
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+ | 0.1297 | 5550 | 0.0003 | - |
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+ | 0.1308 | 5600 | 0.0002 | - |
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+ | 0.1332 | 5700 | 0.0002 | - |
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+ | 0.1343 | 5750 | 0.0003 | - |
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+ | 0.1460 | 6250 | 0.0019 | - |
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+ | 0.1472 | 6300 | 0.0005 | - |
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+ | 0.1507 | 6450 | 0.0003 | - |
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+ | 0.1764 | 7550 | 0.0004 | - |
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+ | 0.1846 | 7900 | 0.0002 | - |
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+ | 0.1857 | 7950 | 0.0002 | - |
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+ | 0.1869 | 8000 | 0.0002 | - |
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+ | 0.1881 | 8050 | 0.0001 | - |
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+ | 0.1892 | 8100 | 0.0002 | - |
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+ | 0.1904 | 8150 | 0.0001 | - |
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+ | 0.1916 | 8200 | 0.0001 | - |
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+ | 0.1928 | 8250 | 0.0001 | - |
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+ | 0.1951 | 8350 | 0.0001 | - |
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+ | 0.1963 | 8400 | 0.0002 | - |
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355
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356
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357
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358
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359
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360
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361
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362
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363
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364
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365
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366
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367
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368
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369
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370
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371
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372
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373
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374
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375
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376
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377
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378
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379
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380
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381
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382
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383
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384
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385
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386
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387
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388
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389
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390
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391
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392
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393
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394
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395
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396
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397
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398
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399
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400
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401
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402
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403
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404
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405
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406
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407
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408
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409
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410
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411
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412
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413
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414
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415
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416
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417
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418
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419
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420
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421
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422
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423
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424
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425
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426
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427
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428
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429
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430
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431
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432
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433
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434
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435
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436
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437
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438
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439
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440
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441
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442
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443
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444
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445
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446
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447
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448
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449
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450
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451
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452
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453
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454
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455
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456
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457
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458
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459
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460
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461
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462
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463
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464
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465
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466
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467
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468
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469
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470
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471
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472
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473
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474
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475
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476
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477
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478
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479
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480
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481
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482
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483
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484
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485
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486
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487
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488
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489
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490
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491
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492
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493
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494
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495
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496
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497
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498
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499
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500
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501
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502
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503
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504
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505
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506
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507
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508
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509
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510
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511
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512
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513
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514
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515
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516
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517
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518
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519
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520
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521
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522
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523
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524
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525
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526
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527
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528
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529
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530
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531
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532
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533
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534
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535
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536
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537
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538
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539
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540
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541
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542
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543
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544
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545
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546
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547
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548
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549
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550
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551
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552
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553
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554
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555
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556
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557
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558
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559
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560
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561
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562
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563
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564
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565
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566
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567
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568
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569
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570
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571
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572
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573
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574
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575
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576
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577
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578
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579
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580
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581
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582
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583
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584
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585
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586
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587
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588
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589
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590
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591
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592
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593
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594
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595
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596
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597
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598
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599
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600
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601
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602
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603
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604
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605
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606
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607
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608
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609
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610
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611
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612
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613
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614
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615
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616
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617
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618
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619
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620
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621
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622
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623
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624
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625
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626
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627
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628
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629
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630
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631
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632
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633
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634
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635
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636
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637
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638
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639
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640
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641
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642
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643
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644
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645
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646
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647
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648
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649
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650
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651
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652
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653
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654
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655
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656
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657
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658
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659
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660
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661
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662
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663
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664
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665
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666
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667
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668
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669
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670
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671
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672
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673
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674
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675
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676
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677
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678
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679
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680
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681
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682
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683
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684
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685
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686
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687
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688
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689
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690
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691
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692
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693
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694
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695
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696
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697
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698
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699
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700
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701
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702
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703
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704
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705
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706
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707
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708
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709
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710
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711
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712
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713
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714
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715
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716
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717
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718
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719
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720
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721
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722
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723
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724
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725
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726
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727
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728
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729
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730
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731
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732
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733
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734
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735
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736
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737
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738
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739
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740
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741
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1042
+
1043
+ ### Framework Versions
1044
+ - Python: 3.10.12
1045
+ - SetFit: 1.0.3
1046
+ - Sentence Transformers: 2.5.1
1047
+ - Transformers: 4.38.1
1048
+ - PyTorch: 2.1.0+cu121
1049
+ - Datasets: 2.18.0
1050
+ - Tokenizers: 0.15.2
1051
+
1052
+ ## Citation
1053
+
1054
+ ### BibTeX
1055
+ ```bibtex
1056
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
1057
+ doi = {10.48550/ARXIV.2209.11055},
1058
+ url = {https://arxiv.org/abs/2209.11055},
1059
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
1060
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
1061
+ title = {Efficient Few-Shot Learning Without Prompts},
1062
+ publisher = {arXiv},
1063
+ year = {2022},
1064
+ copyright = {Creative Commons Attribution 4.0 International}
1065
+ }
1066
+ ```
1067
+
1068
+ <!--
1069
+ ## Glossary
1070
+
1071
+ *Clearly define terms in order to be accessible across audiences.*
1072
+ -->
1073
+
1074
+ <!--
1075
+ ## Model Card Authors
1076
+
1077
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
1078
+ -->
1079
+
1080
+ <!--
1081
+ ## Model Card Contact
1082
+
1083
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
1084
+ -->
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+ "path": "1_Pooling",
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+ "type": "sentence_transformers.models.Pooling"
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+ },
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+ {
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+ "idx": 2,
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+ "name": "2",
17
+ "path": "2_Normalize",
18
+ "type": "sentence_transformers.models.Normalize"
19
+ }
20
+ ]
sentence_bert_config.json ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ {
2
+ "max_seq_length": 512,
3
+ "do_lower_case": true
4
+ }
special_tokens_map.json ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "cls_token": {
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+ "content": "[CLS]",
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+ "pad_token": {
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false
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+ "sep_token": {
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+ "unk_token": {
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+ "lstrip": false,
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+ "rstrip": false,
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+ }
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+ }
tokenizer.json ADDED
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tokenizer_config.json ADDED
@@ -0,0 +1,57 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "added_tokens_decoder": {
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+ "0": {
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+ "content": "[PAD]",
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+ "101": {
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+ "special": true
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+ }
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+ },
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+ "clean_up_tokenization_spaces": true,
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+ "cls_token": "[CLS]",
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+ "do_basic_tokenize": true,
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+ "do_lower_case": true,
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+ "mask_token": "[MASK]",
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+ "model_max_length": 512,
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+ "never_split": null,
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+ "pad_token": "[PAD]",
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+ "sep_token": "[SEP]",
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+ "strip_accents": null,
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+ "tokenize_chinese_chars": true,
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+ "tokenizer_class": "BertTokenizer",
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+ "unk_token": "[UNK]"
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+ }
vocab.txt ADDED
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