jamiehudson commited on
Commit
8951629
1 Parent(s): 1fffcb6

Push model using huggingface_hub.

Browse files
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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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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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414
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415
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418
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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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448
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449
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470
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491
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501
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504
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509
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510
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511
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527
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530
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532
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535
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536
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539
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549
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553
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555
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556
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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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567
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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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575
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577
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578
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580
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591
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630
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632
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649
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650
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651
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653
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657
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662
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663
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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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673
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675
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677
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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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742
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743
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744
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745
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746
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747
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748
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749
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750
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751
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752
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753
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754
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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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3
+ "content": "[CLS]",
4
+ "lstrip": false,
5
+ "normalized": false,
6
+ "rstrip": false,
7
+ "single_word": false
8
+ },
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+ "mask_token": {
10
+ "content": "[MASK]",
11
+ "lstrip": false,
12
+ "normalized": false,
13
+ "rstrip": false,
14
+ "single_word": false
15
+ },
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+ "pad_token": {
17
+ "content": "[PAD]",
18
+ "lstrip": false,
19
+ "normalized": false,
20
+ "rstrip": false,
21
+ "single_word": false
22
+ },
23
+ "sep_token": {
24
+ "content": "[SEP]",
25
+ "lstrip": false,
26
+ "normalized": false,
27
+ "rstrip": false,
28
+ "single_word": false
29
+ },
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+ "unk_token": {
31
+ "content": "[UNK]",
32
+ "lstrip": false,
33
+ "normalized": false,
34
+ "rstrip": false,
35
+ "single_word": false
36
+ }
37
+ }
tokenizer.json ADDED
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tokenizer_config.json ADDED
@@ -0,0 +1,57 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "added_tokens_decoder": {
3
+ "0": {
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+ "content": "[PAD]",
5
+ "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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+ "special": true
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+ },
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+ "100": {
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+ "content": "[UNK]",
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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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+ "special": true
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+ },
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+ "101": {
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+ "content": "[CLS]",
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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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+ "special": true
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+ },
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+ "102": {
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+ "content": "[SEP]",
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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,
33
+ "special": true
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+ },
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+ "103": {
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+ "content": "[MASK]",
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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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+ "special": true
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+ }
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+ },
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+ "clean_up_tokenization_spaces": true,
45
+ "cls_token": "[CLS]",
46
+ "do_basic_tokenize": true,
47
+ "do_lower_case": true,
48
+ "mask_token": "[MASK]",
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+ "model_max_length": 512,
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+ "never_split": null,
51
+ "pad_token": "[PAD]",
52
+ "sep_token": "[SEP]",
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+ "strip_accents": null,
54
+ "tokenize_chinese_chars": true,
55
+ "tokenizer_class": "BertTokenizer",
56
+ "unk_token": "[UNK]"
57
+ }
vocab.txt ADDED
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