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jamiehudson
/
725_model_v2

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
Model card Files Community
jamiehudson commited on
Commit
0e1eb5a
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1 Parent(s): 2fdfa5a

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Browse files
Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +403 -0
  3. config.json +32 -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": 768,
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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-base-en-v1.5
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+ model-index:
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+ - name: SetFit with BAAI/bge-base-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: 0.8996138996138996
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+ name: Accuracy
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+ - type: f1
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+ value:
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+ - 0.5217391304347826
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+ - 0.5142857142857142
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+ - 0.9478260869565217
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+ name: F1
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+ - type: precision
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+ value:
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+ - 0.42857142857142855
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+ - 0.4090909090909091
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+ - 0.9775784753363229
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+ name: Precision
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+ - type: recall
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+ value:
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+ - 0.6666666666666666
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+ - 0.6923076923076923
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+ - 0.919831223628692
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+ name: Recall
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+ ---
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+
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+ # SetFit with BAAI/bge-base-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-base-en-v1.5](https://huggingface.co/BAAI/bge-base-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-base-en-v1.5](https://huggingface.co/BAAI/bge-base-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>'it might sound strange, but in my opinion, sams intelligence intimidates him from expressing himself and creating personal art. for example, since product is a masterpiece in the sense, the bar is set very high, so he might even subconsciously be unable to put anything out less'</li><li>'lately, i really enjoy the genre of joke that makes you say the punchline in your head.'</li><li>'any idea in regard to the product product not being seen? i have 1 device with it, the rest are missing it. same wufb policies.'</li></ul> |
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+ | pit | <ul><li>"brand or brand are behaving like lazy interns. when you need something useful from them like researching and consolidating a large bunch of information they'll just tell you to look it up yourself or right away refuse to do the work. useless."</li><li>'the moment i found out what exactly product does i just uninstalled product and went back to 10'</li><li>"at least 80% of the product stuff posted here has produced erroneous results, and many have utilized ip theft/copyright infringement in informing the model. we're not going to spend community time on it at this point."</li></ul> |
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+ | peak | <ul><li>"man, product/whatever is my new best friend. i like product but the integration of product into office and product is a lot of fun. i just spent the day feeding it my training presentation i'm preparing in my day job and it was very helpful. almost better than humans."</li><li>"excited to share my experience with product, an incredible language model by brand! from answering questions to creative writing, it's a powerful tool that amazes me every time."</li><li>'product in product is a game changer!! here is a list of things it can do: it can answer your questions in natural language. it can summarize content to give you a brief overview it can adjust your pcs settings it can help troubleshoot issues. 1/2'</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** | 0.8996 | [0.5217391304347826, 0.5142857142857142, 0.9478260869565217] | [0.42857142857142855, 0.4090909090909091, 0.9775784753363229] | [0.6666666666666666, 0.6923076923076923, 0.919831223628692] |
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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_v2")
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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 | 5 | 29.1484 | 90 |
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+
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+ | Label | Training Sample Count |
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+ |:--------|:----------------------|
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+ | pit | 44 |
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+ | peak | 62 |
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+ | neither | 150 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (32, 32)
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+ - num_epochs: (3, 3)
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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 | 0.2383 | - |
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+ | 0.0119 | 50 | 0.2395 | - |
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+ | 0.0237 | 100 | 0.2129 | - |
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+ | 0.0356 | 150 | 0.1317 | - |
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+ | 0.0474 | 200 | 0.0695 | - |
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+ | 0.0593 | 250 | 0.01 | - |
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+ | 0.0711 | 300 | 0.0063 | - |
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+ | 0.0830 | 350 | 0.0028 | - |
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+ | 0.0948 | 400 | 0.0026 | - |
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+ | 0.1067 | 450 | 0.0021 | - |
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+ | 0.1185 | 500 | 0.0018 | - |
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+ | 0.1304 | 550 | 0.0016 | - |
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+ | 0.1422 | 600 | 0.0014 | - |
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+ | 0.1541 | 650 | 0.0015 | - |
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+ | 0.1659 | 700 | 0.0013 | - |
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+ | 0.1778 | 750 | 0.0012 | - |
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+ | 0.1896 | 800 | 0.0012 | - |
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+ | 0.2015 | 850 | 0.0012 | - |
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+ | 0.2133 | 900 | 0.0011 | - |
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+ | 0.2252 | 950 | 0.0011 | - |
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+ | 0.2370 | 1000 | 0.0009 | - |
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+ | 0.2489 | 1050 | 0.001 | - |
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+ | 0.2607 | 1100 | 0.0009 | - |
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+ | 0.2726 | 1150 | 0.0008 | - |
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+ | 0.2844 | 1200 | 0.0008 | - |
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+ | 0.2963 | 1250 | 0.0009 | - |
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+ | 0.3081 | 1300 | 0.0008 | - |
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+ | 0.3200 | 1350 | 0.0007 | - |
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+ | 0.3318 | 1400 | 0.0007 | - |
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+ | 0.3437 | 1450 | 0.0007 | - |
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+ | 0.3555 | 1500 | 0.0006 | - |
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+ | 0.3674 | 1550 | 0.0007 | - |
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+ | 0.3792 | 1600 | 0.0007 | - |
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+ | 0.3911 | 1650 | 0.0008 | - |
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+ | 0.4029 | 1700 | 0.0006 | - |
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+ | 0.4148 | 1750 | 0.0006 | - |
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+ | 0.4266 | 1800 | 0.0006 | - |
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+ | 0.4385 | 1850 | 0.0006 | - |
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+ | 0.4503 | 1900 | 0.0006 | - |
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+ | 0.4622 | 1950 | 0.0006 | - |
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+ | 0.4740 | 2000 | 0.0006 | - |
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+ | 0.4859 | 2050 | 0.0005 | - |
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+ | 0.4977 | 2100 | 0.0006 | - |
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+ | 0.5096 | 2150 | 0.0006 | - |
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+ | 0.5215 | 2200 | 0.0005 | - |
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+ | 0.5333 | 2250 | 0.0005 | - |
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+ | 0.5452 | 2300 | 0.0005 | - |
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+ | 0.5570 | 2350 | 0.0006 | - |
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+ | 0.5689 | 2400 | 0.0005 | - |
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+ | 0.5807 | 2450 | 0.0005 | - |
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+ | 0.5926 | 2500 | 0.0006 | - |
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+ | 0.6044 | 2550 | 0.0006 | - |
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+ | 0.6163 | 2600 | 0.0005 | - |
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+ | 0.6281 | 2650 | 0.0005 | - |
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+ | 0.6400 | 2700 | 0.0005 | - |
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+ | 0.6518 | 2750 | 0.0005 | - |
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+ | 0.6637 | 2800 | 0.0005 | - |
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+ | 0.6755 | 2850 | 0.0005 | - |
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+ | 0.6874 | 2900 | 0.0005 | - |
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+ | 0.6992 | 2950 | 0.0004 | - |
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+ | 0.7111 | 3000 | 0.0004 | - |
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+ | 0.7229 | 3050 | 0.0004 | - |
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+ | 0.7348 | 3100 | 0.0005 | - |
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+ | 0.7466 | 3150 | 0.0005 | - |
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+ | 0.7585 | 3200 | 0.0005 | - |
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+ | 0.7703 | 3250 | 0.0004 | - |
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+ | 0.7822 | 3300 | 0.0004 | - |
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+ | 0.7940 | 3350 | 0.0004 | - |
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+ | 0.8059 | 3400 | 0.0004 | - |
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+ | 0.8177 | 3450 | 0.0004 | - |
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+ | 0.8296 | 3500 | 0.0004 | - |
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+ | 0.8414 | 3550 | 0.0004 | - |
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+ | 0.8533 | 3600 | 0.0004 | - |
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+ | 0.8651 | 3650 | 0.0004 | - |
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+ | 0.8770 | 3700 | 0.0004 | - |
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+ | 0.8888 | 3750 | 0.0004 | - |
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+ | 0.9007 | 3800 | 0.0004 | - |
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+ | 0.9125 | 3850 | 0.0004 | - |
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+ | 0.9244 | 3900 | 0.0005 | - |
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+ | 0.9362 | 3950 | 0.0004 | - |
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+ | 0.9481 | 4000 | 0.0004 | - |
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+ | 0.9599 | 4050 | 0.0004 | - |
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+ | 0.9718 | 4100 | 0.0004 | - |
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+ | 0.9836 | 4150 | 0.0004 | - |
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+ | 0.9955 | 4200 | 0.0004 | - |
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+ | 0.0000 | 1 | 0.2717 | - |
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+ | 0.0013 | 50 | 0.0686 | - |
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+ | 0.0026 | 100 | 0.088 | - |
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+ | 0.0000 | 1 | 0.1796 | - |
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+ | 0.0013 | 50 | 0.0584 | - |
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+ | 0.0026 | 100 | 0.1018 | - |
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+ | 0.0039 | 150 | 0.128 | - |
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+ | 0.0052 | 200 | 0.0761 | - |
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+ | 0.0065 | 250 | 0.0216 | - |
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+ | 0.0078 | 300 | 0.1652 | - |
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+ | 0.0091 | 350 | 0.0384 | - |
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+ | 0.0104 | 400 | 0.0062 | - |
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+ | 0.0117 | 450 | 0.0442 | - |
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+ | 0.0130 | 500 | 0.0452 | - |
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+ | 0.0143 | 550 | 0.0081 | - |
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+ | 0.0156 | 600 | 0.0205 | - |
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+ | 0.0169 | 650 | 0.0125 | - |
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+ | 0.0182 | 700 | 0.0012 | - |
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+ | 0.0195 | 750 | 0.0011 | - |
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+ | 0.0208 | 800 | 0.0315 | - |
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+ | 0.0221 | 850 | 0.0009 | - |
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+ | 0.0009 | 1 | 0.0006 | - |
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+ | 0.0429 | 50 | 0.0008 | - |
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+ | 0.0858 | 100 | 0.0005 | - |
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+ | 0.1288 | 150 | 0.0015 | - |
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+ | 0.1717 | 200 | 0.0013 | - |
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+ | 0.2146 | 250 | 0.0237 | - |
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+ | 0.2575 | 300 | 0.0304 | - |
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+ | 0.3004 | 350 | 0.0005 | - |
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+ | 0.3433 | 400 | 0.0013 | - |
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+ | 0.3863 | 450 | 0.03 | - |
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+ | 0.4292 | 500 | 0.0005 | - |
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+ | 0.4721 | 550 | 0.0006 | - |
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+ | 0.5150 | 600 | 0.0005 | - |
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+ | 0.5579 | 650 | 0.0005 | - |
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+ | 0.6009 | 700 | 0.0004 | - |
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+ | 0.6438 | 750 | 0.0004 | - |
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+ | 0.6867 | 800 | 0.0004 | - |
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+ | 0.7296 | 850 | 0.0004 | - |
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+ | 0.7725 | 900 | 0.0004 | - |
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+ | 0.8155 | 950 | 0.0003 | - |
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+ | 0.8584 | 1000 | 0.0004 | - |
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+ | 0.9013 | 1050 | 0.0003 | - |
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+ | 0.9442 | 1100 | 0.0004 | - |
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+ | 0.9871 | 1150 | 0.0003 | - |
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+ | 1.0300 | 1200 | 0.0003 | - |
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+ | 1.0730 | 1250 | 0.0004 | - |
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+ | 1.1159 | 1300 | 0.0003 | - |
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+ | 1.1588 | 1350 | 0.0005 | - |
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+ | 1.2017 | 1400 | 0.0003 | - |
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+ | 1.2446 | 1450 | 0.0003 | - |
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+ | 1.2876 | 1500 | 0.0003 | - |
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+ | 1.3305 | 1550 | 0.0003 | - |
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+ | 1.3734 | 1600 | 0.0003 | - |
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+ | 1.4163 | 1650 | 0.0003 | - |
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+ | 1.4592 | 1700 | 0.0003 | - |
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+ | 1.5021 | 1750 | 0.0005 | - |
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+ | 1.5451 | 1800 | 0.0003 | - |
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+ | 1.5880 | 1850 | 0.0003 | - |
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+ | 1.6309 | 1900 | 0.0003 | - |
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+ | 1.6738 | 1950 | 0.0005 | - |
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+ | 1.7167 | 2000 | 0.0003 | - |
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+ | 1.7597 | 2050 | 0.0007 | - |
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+ | 1.8026 | 2100 | 0.0003 | - |
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+ | 1.8455 | 2150 | 0.0003 | - |
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+ | 1.8884 | 2200 | 0.0003 | - |
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+ | 1.9313 | 2250 | 0.0003 | - |
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+ | 1.9742 | 2300 | 0.0003 | - |
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+ | 2.0172 | 2350 | 0.0003 | - |
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+ | 2.0601 | 2400 | 0.0003 | - |
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+ | 2.1030 | 2450 | 0.0003 | - |
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+ | 2.1459 | 2500 | 0.0003 | - |
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+ | 2.1888 | 2550 | 0.0002 | - |
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+ | 2.2318 | 2600 | 0.0003 | - |
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+ | 2.2747 | 2650 | 0.0004 | - |
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+ | 2.3176 | 2700 | 0.0002 | - |
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+ | 2.3605 | 2750 | 0.0003 | - |
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+ | 2.4034 | 2800 | 0.0002 | - |
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+ | 2.4464 | 2850 | 0.0002 | - |
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+ | 2.4893 | 2900 | 0.0002 | - |
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+ | 2.5322 | 2950 | 0.0002 | - |
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+ | 2.5751 | 3000 | 0.0002 | - |
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+ | 2.6180 | 3050 | 0.0004 | - |
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+ | 2.6609 | 3100 | 0.0004 | - |
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+ | 2.7039 | 3150 | 0.0003 | - |
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+ | 2.7468 | 3200 | 0.0003 | - |
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+ | 2.7897 | 3250 | 0.0003 | - |
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+ | 2.8326 | 3300 | 0.0003 | - |
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+ | 2.8755 | 3350 | 0.0003 | - |
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+ | 2.9185 | 3400 | 0.0003 | - |
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+ | 2.9614 | 3450 | 0.0005 | - |
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+
362
+ ### Framework Versions
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+ - Python: 3.10.12
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+ - SetFit: 1.0.3
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+ - Sentence Transformers: 2.5.1
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+ - Transformers: 4.38.1
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+ - PyTorch: 2.1.0+cu121
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+ - Datasets: 2.18.0
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+ - Tokenizers: 0.15.2
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+
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+ ## Citation
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+
373
+ ### 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.*
397
+ -->
398
+
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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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