Upload 12 files
Browse files- README.md +555 -3
- config.json +24 -0
- config_sentence_transformers.json +9 -0
- config_setfit.json +9 -0
- model.safetensors +3 -0
- model_head.pkl +3 -0
- modules.json +14 -0
- sentence_bert_config.json +4 -0
- special_tokens_map.json +51 -0
- tokenizer.json +0 -0
- tokenizer_config.json +59 -0
- vocab.txt +0 -0
README.md
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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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- absa
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- sentence-transformers
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- text-classification
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- generated_from_setfit_trainer
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base_model: sentence-transformers/paraphrase-mpnet-base-v2
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metrics:
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- accuracy
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widget:
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- text: food portions:The food portions are quite filling, but not too much.
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- text: waiters:The waiters are quite alert in helping customers, but cannot always
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answer all questions in detail.
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- text: experience:The atmosphere here is pleasant, although it doesn't provide an
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extraordinary experience.
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- text: food:The food does not have a distinctive taste.
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- text: restaurant atmosphere:The restaurant atmosphere is too stiff and unpleasant.
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pipeline_tag: text-classification
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inference: false
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model-index:
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- name: SetFit Aspect Model with sentence-transformers/paraphrase-mpnet-base-v2
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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: 1.0
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name: Accuracy
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---
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# SetFit Aspect Model with sentence-transformers/paraphrase-mpnet-base-v2
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This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) 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. In particular, this model is in charge of filtering aspect span candidates.
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The model has been trained using an efficient few-shot learning technique that involves:
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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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This model was trained within the context of a larger system for ABSA, which looks like so:
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1. Use a spaCy model to select possible aspect span candidates.
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2. **Use this SetFit model to filter these possible aspect span candidates.**
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3. Use a SetFit model to classify the filtered aspect span candidates.
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+
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## Model Details
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### Model Description
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- **Model Type:** SetFit
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- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
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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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- **spaCy Model:** en_core_web_lg
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- **SetFitABSA Aspect Model:** [models/en-setfit-absa-model-aspect](https://huggingface.co/models/en-setfit-absa-model-aspect)
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- **SetFitABSA Polarity Model:** [models/en-setfit-absa-model-polarity](https://huggingface.co/models/en-setfit-absa-model-polarity)
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- **Maximum Sequence Length:** 512 tokens
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- **Number of Classes:** 2 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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| no aspect | <ul><li>'food:The food is really delicious! The meat is tender and the spices are well seasoned. I will definitely come back again.'</li><li>'meat:The food is really delicious! The meat is tender and the spices are well seasoned. I will definitely come back again.'</li><li>'spices:The food is really delicious! The meat is tender and the spices are well seasoned. I will definitely come back again.'</li></ul> |
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| aspect | <ul><li>'Service:Service is standard, nothing extraordinary.'</li><li>'Service:Service from the staff is very friendly.'</li><li>'Service:Service from the staff is very fast and professional.'</li></ul> |
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+
|
80 |
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## Evaluation
|
81 |
+
|
82 |
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### Metrics
|
83 |
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| Label | Accuracy |
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84 |
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|:--------|:---------|
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| **all** | 1.0 |
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+
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## Uses
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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 AbsaModel
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# Download from the 🤗 Hub
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model = AbsaModel.from_pretrained(
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"models/en-setfit-absa-model-aspect",
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"models/en-setfit-absa-model-polarity",
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)
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# Run inference
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preds = model("The food was great, but the venue is just way too busy.")
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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
|
119 |
+
|
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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
|
125 |
+
|
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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
|
131 |
+
|
132 |
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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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136 |
+
|
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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 | 4 | 14.3487 | 72 |
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+
|
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+
| Label | Training Sample Count |
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|:----------|:----------------------|
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| no aspect | 1701 |
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| aspect | 14 |
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+
|
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### Training Hyperparameters
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- batch_size: (4, 4)
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- num_epochs: (1, 1)
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- max_steps: -1
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- sampling_strategy: oversampling
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- num_iterations: 20
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- body_learning_rate: (2e-05, 1e-05)
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- head_learning_rate: 0.01
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- loss: CosineSimilarityLoss
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- distance_metric: cosine_distance
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- margin: 0.25
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- end_to_end: False
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- use_amp: False
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- warmup_proportion: 0.1
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- seed: 42
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+
- eval_max_steps: -1
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- load_best_model_at_end: False
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+
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+
### Training Results
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| Epoch | Step | Training Loss | Validation Loss |
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|:------:|:-----:|:-------------:|:---------------:|
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| 0.0001 | 1 | 0.34 | - |
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| 0.0029 | 50 | 0.318 | - |
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| 0.0058 | 100 | 0.2344 | - |
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| 0.0087 | 150 | 0.1925 | - |
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| 0.0117 | 200 | 0.1893 | - |
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| 0.0146 | 250 | 0.014 | - |
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| 0.0175 | 300 | 0.0017 | - |
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| 0.0204 | 350 | 0.0041 | - |
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| 0.0233 | 400 | 0.0008 | - |
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| 0.0262 | 450 | 0.0008 | - |
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| 0.0292 | 500 | 0.0003 | - |
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| 0.0321 | 550 | 0.0003 | - |
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| 0.0350 | 600 | 0.0004 | - |
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| 0.0379 | 650 | 0.0004 | - |
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| 0.0408 | 700 | 0.0004 | - |
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| 0.0437 | 750 | 0.0008 | - |
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210 |
+
| 0.1224 | 2100 | 0.0 | - |
|
211 |
+
| 0.1254 | 2150 | 0.0006 | - |
|
212 |
+
| 0.1283 | 2200 | 0.0002 | - |
|
213 |
+
| 0.1312 | 2250 | 0.0 | - |
|
214 |
+
| 0.1341 | 2300 | 0.0 | - |
|
215 |
+
| 0.1370 | 2350 | 0.2106 | - |
|
216 |
+
| 0.1399 | 2400 | 0.0 | - |
|
217 |
+
| 0.1429 | 2450 | 0.0001 | - |
|
218 |
+
| 0.1458 | 2500 | 0.0001 | - |
|
219 |
+
| 0.1487 | 2550 | 0.0 | - |
|
220 |
+
| 0.1516 | 2600 | 0.0 | - |
|
221 |
+
| 0.1545 | 2650 | 0.0 | - |
|
222 |
+
| 0.1574 | 2700 | 0.0 | - |
|
223 |
+
| 0.1603 | 2750 | 0.0 | - |
|
224 |
+
| 0.1633 | 2800 | 0.0 | - |
|
225 |
+
| 0.1662 | 2850 | 0.0001 | - |
|
226 |
+
| 0.1691 | 2900 | 0.0 | - |
|
227 |
+
| 0.1720 | 2950 | 0.0 | - |
|
228 |
+
| 0.1749 | 3000 | 0.0 | - |
|
229 |
+
| 0.1778 | 3050 | 0.0001 | - |
|
230 |
+
| 0.1808 | 3100 | 0.0 | - |
|
231 |
+
| 0.1837 | 3150 | 0.0 | - |
|
232 |
+
| 0.1866 | 3200 | 0.0001 | - |
|
233 |
+
| 0.1895 | 3250 | 0.0 | - |
|
234 |
+
| 0.1924 | 3300 | 0.0001 | - |
|
235 |
+
| 0.1953 | 3350 | 0.0001 | - |
|
236 |
+
| 0.1983 | 3400 | 0.0 | - |
|
237 |
+
| 0.2012 | 3450 | 0.0 | - |
|
238 |
+
| 0.2041 | 3500 | 0.0 | - |
|
239 |
+
| 0.2070 | 3550 | 0.0 | - |
|
240 |
+
| 0.2099 | 3600 | 0.0 | - |
|
241 |
+
| 0.2128 | 3650 | 0.0 | - |
|
242 |
+
| 0.2157 | 3700 | 0.0 | - |
|
243 |
+
| 0.2187 | 3750 | 0.0 | - |
|
244 |
+
| 0.2216 | 3800 | 0.0 | - |
|
245 |
+
| 0.2245 | 3850 | 0.0 | - |
|
246 |
+
| 0.2274 | 3900 | 0.0 | - |
|
247 |
+
| 0.2303 | 3950 | 0.0 | - |
|
248 |
+
| 0.2332 | 4000 | 0.0 | - |
|
249 |
+
| 0.2362 | 4050 | 0.0 | - |
|
250 |
+
| 0.2391 | 4100 | 0.0 | - |
|
251 |
+
| 0.2420 | 4150 | 0.0 | - |
|
252 |
+
| 0.2449 | 4200 | 0.0 | - |
|
253 |
+
| 0.2478 | 4250 | 0.0 | - |
|
254 |
+
| 0.2507 | 4300 | 0.0 | - |
|
255 |
+
| 0.2536 | 4350 | 0.0 | - |
|
256 |
+
| 0.2566 | 4400 | 0.0 | - |
|
257 |
+
| 0.2595 | 4450 | 0.0 | - |
|
258 |
+
| 0.2624 | 4500 | 0.0 | - |
|
259 |
+
| 0.2653 | 4550 | 0.0 | - |
|
260 |
+
| 0.2682 | 4600 | 0.0 | - |
|
261 |
+
| 0.2711 | 4650 | 0.0 | - |
|
262 |
+
| 0.2741 | 4700 | 0.0001 | - |
|
263 |
+
| 0.2770 | 4750 | 0.0 | - |
|
264 |
+
| 0.2799 | 4800 | 0.0 | - |
|
265 |
+
| 0.2828 | 4850 | 0.0 | - |
|
266 |
+
| 0.2857 | 4900 | 0.0 | - |
|
267 |
+
| 0.2886 | 4950 | 0.0 | - |
|
268 |
+
| 0.2915 | 5000 | 0.0 | - |
|
269 |
+
| 0.2945 | 5050 | 0.0 | - |
|
270 |
+
| 0.2974 | 5100 | 0.0 | - |
|
271 |
+
| 0.3003 | 5150 | 0.0 | - |
|
272 |
+
| 0.3032 | 5200 | 0.0 | - |
|
273 |
+
| 0.3061 | 5250 | 0.0 | - |
|
274 |
+
| 0.3090 | 5300 | 0.0 | - |
|
275 |
+
| 0.3120 | 5350 | 0.0 | - |
|
276 |
+
| 0.3149 | 5400 | 0.0 | - |
|
277 |
+
| 0.3178 | 5450 | 0.0 | - |
|
278 |
+
| 0.3207 | 5500 | 0.0 | - |
|
279 |
+
| 0.3236 | 5550 | 0.0 | - |
|
280 |
+
| 0.3265 | 5600 | 0.0 | - |
|
281 |
+
| 0.3294 | 5650 | 0.0 | - |
|
282 |
+
| 0.3324 | 5700 | 0.0 | - |
|
283 |
+
| 0.3353 | 5750 | 0.0 | - |
|
284 |
+
| 0.3382 | 5800 | 0.0 | - |
|
285 |
+
| 0.3411 | 5850 | 0.0 | - |
|
286 |
+
| 0.3440 | 5900 | 0.0 | - |
|
287 |
+
| 0.3469 | 5950 | 0.0 | - |
|
288 |
+
| 0.3499 | 6000 | 0.0 | - |
|
289 |
+
| 0.3528 | 6050 | 0.0 | - |
|
290 |
+
| 0.3557 | 6100 | 0.0 | - |
|
291 |
+
| 0.3586 | 6150 | 0.0 | - |
|
292 |
+
| 0.3615 | 6200 | 0.0 | - |
|
293 |
+
| 0.3644 | 6250 | 0.0 | - |
|
294 |
+
| 0.3673 | 6300 | 0.0 | - |
|
295 |
+
| 0.3703 | 6350 | 0.0 | - |
|
296 |
+
| 0.3732 | 6400 | 0.0001 | - |
|
297 |
+
| 0.3761 | 6450 | 0.0 | - |
|
298 |
+
| 0.3790 | 6500 | 0.0 | - |
|
299 |
+
| 0.3819 | 6550 | 0.0 | - |
|
300 |
+
| 0.3848 | 6600 | 0.0 | - |
|
301 |
+
| 0.3878 | 6650 | 0.0 | - |
|
302 |
+
| 0.3907 | 6700 | 0.0 | - |
|
303 |
+
| 0.3936 | 6750 | 0.0 | - |
|
304 |
+
| 0.3965 | 6800 | 0.0 | - |
|
305 |
+
| 0.3994 | 6850 | 0.0 | - |
|
306 |
+
| 0.4023 | 6900 | 0.0 | - |
|
307 |
+
| 0.4052 | 6950 | 0.0 | - |
|
308 |
+
| 0.4082 | 7000 | 0.0 | - |
|
309 |
+
| 0.4111 | 7050 | 0.0 | - |
|
310 |
+
| 0.4140 | 7100 | 0.0001 | - |
|
311 |
+
| 0.4169 | 7150 | 0.0 | - |
|
312 |
+
| 0.4198 | 7200 | 0.0 | - |
|
313 |
+
| 0.4227 | 7250 | 0.0 | - |
|
314 |
+
| 0.4257 | 7300 | 0.0 | - |
|
315 |
+
| 0.4286 | 7350 | 0.0 | - |
|
316 |
+
| 0.4315 | 7400 | 0.0 | - |
|
317 |
+
| 0.4344 | 7450 | 0.0 | - |
|
318 |
+
| 0.4373 | 7500 | 0.0 | - |
|
319 |
+
| 0.4402 | 7550 | 0.0 | - |
|
320 |
+
| 0.4431 | 7600 | 0.0 | - |
|
321 |
+
| 0.4461 | 7650 | 0.0 | - |
|
322 |
+
| 0.4490 | 7700 | 0.0 | - |
|
323 |
+
| 0.4519 | 7750 | 0.0 | - |
|
324 |
+
| 0.4548 | 7800 | 0.0 | - |
|
325 |
+
| 0.4577 | 7850 | 0.0 | - |
|
326 |
+
| 0.4606 | 7900 | 0.0 | - |
|
327 |
+
| 0.4636 | 7950 | 0.0 | - |
|
328 |
+
| 0.4665 | 8000 | 0.0 | - |
|
329 |
+
| 0.4694 | 8050 | 0.0 | - |
|
330 |
+
| 0.4723 | 8100 | 0.0 | - |
|
331 |
+
| 0.4752 | 8150 | 0.0 | - |
|
332 |
+
| 0.4781 | 8200 | 0.0 | - |
|
333 |
+
| 0.4810 | 8250 | 0.0 | - |
|
334 |
+
| 0.4840 | 8300 | 0.0 | - |
|
335 |
+
| 0.4869 | 8350 | 0.0001 | - |
|
336 |
+
| 0.4898 | 8400 | 0.0 | - |
|
337 |
+
| 0.4927 | 8450 | 0.0 | - |
|
338 |
+
| 0.4956 | 8500 | 0.0 | - |
|
339 |
+
| 0.4985 | 8550 | 0.0 | - |
|
340 |
+
| 0.5015 | 8600 | 0.0 | - |
|
341 |
+
| 0.5044 | 8650 | 0.0 | - |
|
342 |
+
| 0.5073 | 8700 | 0.0 | - |
|
343 |
+
| 0.5102 | 8750 | 0.0 | - |
|
344 |
+
| 0.5131 | 8800 | 0.0 | - |
|
345 |
+
| 0.5160 | 8850 | 0.0 | - |
|
346 |
+
| 0.5190 | 8900 | 0.0 | - |
|
347 |
+
| 0.5219 | 8950 | 0.0 | - |
|
348 |
+
| 0.5248 | 9000 | 0.0 | - |
|
349 |
+
| 0.5277 | 9050 | 0.0 | - |
|
350 |
+
| 0.5306 | 9100 | 0.0 | - |
|
351 |
+
| 0.5335 | 9150 | 0.0 | - |
|
352 |
+
| 0.5364 | 9200 | 0.0 | - |
|
353 |
+
| 0.5394 | 9250 | 0.0 | - |
|
354 |
+
| 0.5423 | 9300 | 0.0 | - |
|
355 |
+
| 0.5452 | 9350 | 0.0 | - |
|
356 |
+
| 0.5481 | 9400 | 0.0 | - |
|
357 |
+
| 0.5510 | 9450 | 0.0 | - |
|
358 |
+
| 0.5539 | 9500 | 0.0 | - |
|
359 |
+
| 0.5569 | 9550 | 0.0 | - |
|
360 |
+
| 0.5598 | 9600 | 0.0 | - |
|
361 |
+
| 0.5627 | 9650 | 0.0 | - |
|
362 |
+
| 0.5656 | 9700 | 0.0 | - |
|
363 |
+
| 0.5685 | 9750 | 0.0 | - |
|
364 |
+
| 0.5714 | 9800 | 0.0 | - |
|
365 |
+
| 0.5743 | 9850 | 0.0 | - |
|
366 |
+
| 0.5773 | 9900 | 0.0 | - |
|
367 |
+
| 0.5802 | 9950 | 0.0 | - |
|
368 |
+
| 0.5831 | 10000 | 0.0 | - |
|
369 |
+
| 0.5860 | 10050 | 0.0 | - |
|
370 |
+
| 0.5889 | 10100 | 0.0 | - |
|
371 |
+
| 0.5918 | 10150 | 0.0 | - |
|
372 |
+
| 0.5948 | 10200 | 0.0 | - |
|
373 |
+
| 0.5977 | 10250 | 0.0 | - |
|
374 |
+
| 0.6006 | 10300 | 0.0 | - |
|
375 |
+
| 0.6035 | 10350 | 0.0 | - |
|
376 |
+
| 0.6064 | 10400 | 0.0 | - |
|
377 |
+
| 0.6093 | 10450 | 0.0 | - |
|
378 |
+
| 0.6122 | 10500 | 0.0 | - |
|
379 |
+
| 0.6152 | 10550 | 0.0 | - |
|
380 |
+
| 0.6181 | 10600 | 0.0 | - |
|
381 |
+
| 0.6210 | 10650 | 0.0 | - |
|
382 |
+
| 0.6239 | 10700 | 0.0 | - |
|
383 |
+
| 0.6268 | 10750 | 0.0 | - |
|
384 |
+
| 0.6297 | 10800 | 0.0 | - |
|
385 |
+
| 0.6327 | 10850 | 0.0 | - |
|
386 |
+
| 0.6356 | 10900 | 0.0 | - |
|
387 |
+
| 0.6385 | 10950 | 0.0 | - |
|
388 |
+
| 0.6414 | 11000 | 0.0 | - |
|
389 |
+
| 0.6443 | 11050 | 0.0 | - |
|
390 |
+
| 0.6472 | 11100 | 0.0 | - |
|
391 |
+
| 0.6501 | 11150 | 0.0 | - |
|
392 |
+
| 0.6531 | 11200 | 0.0 | - |
|
393 |
+
| 0.6560 | 11250 | 0.0 | - |
|
394 |
+
| 0.6589 | 11300 | 0.0 | - |
|
395 |
+
| 0.6618 | 11350 | 0.0 | - |
|
396 |
+
| 0.6647 | 11400 | 0.0 | - |
|
397 |
+
| 0.6676 | 11450 | 0.0 | - |
|
398 |
+
| 0.6706 | 11500 | 0.0 | - |
|
399 |
+
| 0.6735 | 11550 | 0.0 | - |
|
400 |
+
| 0.6764 | 11600 | 0.0 | - |
|
401 |
+
| 0.6793 | 11650 | 0.0 | - |
|
402 |
+
| 0.6822 | 11700 | 0.0 | - |
|
403 |
+
| 0.6851 | 11750 | 0.0 | - |
|
404 |
+
| 0.6880 | 11800 | 0.0 | - |
|
405 |
+
| 0.6910 | 11850 | 0.0 | - |
|
406 |
+
| 0.6939 | 11900 | 0.0 | - |
|
407 |
+
| 0.6968 | 11950 | 0.0 | - |
|
408 |
+
| 0.6997 | 12000 | 0.0 | - |
|
409 |
+
| 0.7026 | 12050 | 0.0 | - |
|
410 |
+
| 0.7055 | 12100 | 0.0 | - |
|
411 |
+
| 0.7085 | 12150 | 0.0 | - |
|
412 |
+
| 0.7114 | 12200 | 0.0 | - |
|
413 |
+
| 0.7143 | 12250 | 0.0 | - |
|
414 |
+
| 0.7172 | 12300 | 0.0 | - |
|
415 |
+
| 0.7201 | 12350 | 0.0 | - |
|
416 |
+
| 0.7230 | 12400 | 0.0 | - |
|
417 |
+
| 0.7259 | 12450 | 0.0 | - |
|
418 |
+
| 0.7289 | 12500 | 0.0 | - |
|
419 |
+
| 0.7318 | 12550 | 0.0 | - |
|
420 |
+
| 0.7347 | 12600 | 0.0 | - |
|
421 |
+
| 0.7376 | 12650 | 0.0 | - |
|
422 |
+
| 0.7405 | 12700 | 0.0 | - |
|
423 |
+
| 0.7434 | 12750 | 0.0 | - |
|
424 |
+
| 0.7464 | 12800 | 0.0 | - |
|
425 |
+
| 0.7493 | 12850 | 0.0 | - |
|
426 |
+
| 0.7522 | 12900 | 0.0 | - |
|
427 |
+
| 0.7551 | 12950 | 0.0 | - |
|
428 |
+
| 0.7580 | 13000 | 0.0 | - |
|
429 |
+
| 0.7609 | 13050 | 0.0 | - |
|
430 |
+
| 0.7638 | 13100 | 0.0 | - |
|
431 |
+
| 0.7668 | 13150 | 0.0 | - |
|
432 |
+
| 0.7697 | 13200 | 0.0 | - |
|
433 |
+
| 0.7726 | 13250 | 0.0 | - |
|
434 |
+
| 0.7755 | 13300 | 0.0 | - |
|
435 |
+
| 0.7784 | 13350 | 0.0 | - |
|
436 |
+
| 0.7813 | 13400 | 0.0 | - |
|
437 |
+
| 0.7843 | 13450 | 0.0 | - |
|
438 |
+
| 0.7872 | 13500 | 0.0 | - |
|
439 |
+
| 0.7901 | 13550 | 0.0 | - |
|
440 |
+
| 0.7930 | 13600 | 0.0 | - |
|
441 |
+
| 0.7959 | 13650 | 0.0 | - |
|
442 |
+
| 0.7988 | 13700 | 0.0 | - |
|
443 |
+
| 0.8017 | 13750 | 0.0 | - |
|
444 |
+
| 0.8047 | 13800 | 0.0 | - |
|
445 |
+
| 0.8076 | 13850 | 0.0 | - |
|
446 |
+
| 0.8105 | 13900 | 0.0 | - |
|
447 |
+
| 0.8134 | 13950 | 0.0 | - |
|
448 |
+
| 0.8163 | 14000 | 0.0 | - |
|
449 |
+
| 0.8192 | 14050 | 0.0 | - |
|
450 |
+
| 0.8222 | 14100 | 0.0 | - |
|
451 |
+
| 0.8251 | 14150 | 0.0 | - |
|
452 |
+
| 0.8280 | 14200 | 0.0 | - |
|
453 |
+
| 0.8309 | 14250 | 0.0 | - |
|
454 |
+
| 0.8338 | 14300 | 0.0 | - |
|
455 |
+
| 0.8367 | 14350 | 0.0 | - |
|
456 |
+
| 0.8397 | 14400 | 0.0 | - |
|
457 |
+
| 0.8426 | 14450 | 0.0 | - |
|
458 |
+
| 0.8455 | 14500 | 0.0 | - |
|
459 |
+
| 0.8484 | 14550 | 0.0 | - |
|
460 |
+
| 0.8513 | 14600 | 0.0 | - |
|
461 |
+
| 0.8542 | 14650 | 0.0 | - |
|
462 |
+
| 0.8571 | 14700 | 0.0 | - |
|
463 |
+
| 0.8601 | 14750 | 0.0 | - |
|
464 |
+
| 0.8630 | 14800 | 0.0 | - |
|
465 |
+
| 0.8659 | 14850 | 0.0 | - |
|
466 |
+
| 0.8688 | 14900 | 0.0 | - |
|
467 |
+
| 0.8717 | 14950 | 0.0 | - |
|
468 |
+
| 0.8746 | 15000 | 0.0 | - |
|
469 |
+
| 0.8776 | 15050 | 0.0 | - |
|
470 |
+
| 0.8805 | 15100 | 0.0 | - |
|
471 |
+
| 0.8834 | 15150 | 0.0 | - |
|
472 |
+
| 0.8863 | 15200 | 0.0 | - |
|
473 |
+
| 0.8892 | 15250 | 0.0 | - |
|
474 |
+
| 0.8921 | 15300 | 0.0 | - |
|
475 |
+
| 0.8950 | 15350 | 0.0 | - |
|
476 |
+
| 0.8980 | 15400 | 0.0 | - |
|
477 |
+
| 0.9009 | 15450 | 0.0 | - |
|
478 |
+
| 0.9038 | 15500 | 0.0 | - |
|
479 |
+
| 0.9067 | 15550 | 0.0 | - |
|
480 |
+
| 0.9096 | 15600 | 0.0 | - |
|
481 |
+
| 0.9125 | 15650 | 0.0 | - |
|
482 |
+
| 0.9155 | 15700 | 0.0 | - |
|
483 |
+
| 0.9184 | 15750 | 0.0 | - |
|
484 |
+
| 0.9213 | 15800 | 0.0 | - |
|
485 |
+
| 0.9242 | 15850 | 0.0 | - |
|
486 |
+
| 0.9271 | 15900 | 0.0 | - |
|
487 |
+
| 0.9300 | 15950 | 0.0 | - |
|
488 |
+
| 0.9329 | 16000 | 0.0 | - |
|
489 |
+
| 0.9359 | 16050 | 0.0 | - |
|
490 |
+
| 0.9388 | 16100 | 0.0 | - |
|
491 |
+
| 0.9417 | 16150 | 0.0 | - |
|
492 |
+
| 0.9446 | 16200 | 0.0 | - |
|
493 |
+
| 0.9475 | 16250 | 0.0 | - |
|
494 |
+
| 0.9504 | 16300 | 0.0 | - |
|
495 |
+
| 0.9534 | 16350 | 0.0 | - |
|
496 |
+
| 0.9563 | 16400 | 0.0 | - |
|
497 |
+
| 0.9592 | 16450 | 0.0 | - |
|
498 |
+
| 0.9621 | 16500 | 0.0 | - |
|
499 |
+
| 0.9650 | 16550 | 0.0 | - |
|
500 |
+
| 0.9679 | 16600 | 0.0 | - |
|
501 |
+
| 0.9708 | 16650 | 0.0 | - |
|
502 |
+
| 0.9738 | 16700 | 0.0 | - |
|
503 |
+
| 0.9767 | 16750 | 0.0 | - |
|
504 |
+
| 0.9796 | 16800 | 0.0 | - |
|
505 |
+
| 0.9825 | 16850 | 0.0 | - |
|
506 |
+
| 0.9854 | 16900 | 0.0 | - |
|
507 |
+
| 0.9883 | 16950 | 0.0 | - |
|
508 |
+
| 0.9913 | 17000 | 0.0 | - |
|
509 |
+
| 0.9942 | 17050 | 0.0 | - |
|
510 |
+
| 0.9971 | 17100 | 0.0 | - |
|
511 |
+
| 1.0 | 17150 | 0.0 | - |
|
512 |
+
|
513 |
+
### Framework Versions
|
514 |
+
- Python: 3.10.13
|
515 |
+
- SetFit: 1.0.3
|
516 |
+
- Sentence Transformers: 2.7.0
|
517 |
+
- spaCy: 3.7.4
|
518 |
+
- Transformers: 4.39.3
|
519 |
+
- PyTorch: 2.1.2
|
520 |
+
- Datasets: 2.18.0
|
521 |
+
- Tokenizers: 0.15.2
|
522 |
+
|
523 |
+
## Citation
|
524 |
+
|
525 |
+
### BibTeX
|
526 |
+
```bibtex
|
527 |
+
@article{https://doi.org/10.48550/arxiv.2209.11055,
|
528 |
+
doi = {10.48550/ARXIV.2209.11055},
|
529 |
+
url = {https://arxiv.org/abs/2209.11055},
|
530 |
+
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
|
531 |
+
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
|
532 |
+
title = {Efficient Few-Shot Learning Without Prompts},
|
533 |
+
publisher = {arXiv},
|
534 |
+
year = {2022},
|
535 |
+
copyright = {Creative Commons Attribution 4.0 International}
|
536 |
+
}
|
537 |
+
```
|
538 |
+
|
539 |
+
<!--
|
540 |
+
## Glossary
|
541 |
+
|
542 |
+
*Clearly define terms in order to be accessible across audiences.*
|
543 |
+
-->
|
544 |
+
|
545 |
+
<!--
|
546 |
+
## Model Card Authors
|
547 |
+
|
548 |
+
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
549 |
+
-->
|
550 |
+
|
551 |
+
<!--
|
552 |
+
## Model Card Contact
|
553 |
+
|
554 |
+
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
|
555 |
+
-->
|
config.json
ADDED
@@ -0,0 +1,24 @@
|
|
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|
1 |
+
{
|
2 |
+
"_name_or_path": "sentence-transformers/paraphrase-mpnet-base-v2",
|
3 |
+
"architectures": [
|
4 |
+
"MPNetModel"
|
5 |
+
],
|
6 |
+
"attention_probs_dropout_prob": 0.1,
|
7 |
+
"bos_token_id": 0,
|
8 |
+
"eos_token_id": 2,
|
9 |
+
"hidden_act": "gelu",
|
10 |
+
"hidden_dropout_prob": 0.1,
|
11 |
+
"hidden_size": 768,
|
12 |
+
"initializer_range": 0.02,
|
13 |
+
"intermediate_size": 3072,
|
14 |
+
"layer_norm_eps": 1e-05,
|
15 |
+
"max_position_embeddings": 514,
|
16 |
+
"model_type": "mpnet",
|
17 |
+
"num_attention_heads": 12,
|
18 |
+
"num_hidden_layers": 12,
|
19 |
+
"pad_token_id": 1,
|
20 |
+
"relative_attention_num_buckets": 32,
|
21 |
+
"torch_dtype": "float32",
|
22 |
+
"transformers_version": "4.39.3",
|
23 |
+
"vocab_size": 30527
|
24 |
+
}
|
config_sentence_transformers.json
ADDED
@@ -0,0 +1,9 @@
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"__version__": {
|
3 |
+
"sentence_transformers": "2.0.0",
|
4 |
+
"transformers": "4.7.0",
|
5 |
+
"pytorch": "1.9.0+cu102"
|
6 |
+
},
|
7 |
+
"prompts": {},
|
8 |
+
"default_prompt_name": null
|
9 |
+
}
|
config_setfit.json
ADDED
@@ -0,0 +1,9 @@
|
|
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|
|
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|
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|
|
|
|
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|
|
1 |
+
{
|
2 |
+
"labels": [
|
3 |
+
"no aspect",
|
4 |
+
"aspect"
|
5 |
+
],
|
6 |
+
"spacy_model": "en_core_web_lg",
|
7 |
+
"span_context": 0,
|
8 |
+
"normalize_embeddings": false
|
9 |
+
}
|
model.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:ef44edb83ebb83a813942861abf73da10b810f4ab36a3e79618e486f1895893f
|
3 |
+
size 437967672
|
model_head.pkl
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:c6dce6906de3149da54eff2b6fcf60ebb8bc1e85c3f196ec2af51102969d700a
|
3 |
+
size 6991
|
modules.json
ADDED
@@ -0,0 +1,14 @@
|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
[
|
2 |
+
{
|
3 |
+
"idx": 0,
|
4 |
+
"name": "0",
|
5 |
+
"path": "",
|
6 |
+
"type": "sentence_transformers.models.Transformer"
|
7 |
+
},
|
8 |
+
{
|
9 |
+
"idx": 1,
|
10 |
+
"name": "1",
|
11 |
+
"path": "1_Pooling",
|
12 |
+
"type": "sentence_transformers.models.Pooling"
|
13 |
+
}
|
14 |
+
]
|
sentence_bert_config.json
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"max_seq_length": 512,
|
3 |
+
"do_lower_case": false
|
4 |
+
}
|
special_tokens_map.json
ADDED
@@ -0,0 +1,51 @@
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"bos_token": {
|
3 |
+
"content": "<s>",
|
4 |
+
"lstrip": false,
|
5 |
+
"normalized": false,
|
6 |
+
"rstrip": false,
|
7 |
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"single_word": false
|
8 |
+
},
|
9 |
+
"cls_token": {
|
10 |
+
"content": "<s>",
|
11 |
+
"lstrip": false,
|
12 |
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"normalized": false,
|
13 |
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"rstrip": false,
|
14 |
+
"single_word": false
|
15 |
+
},
|
16 |
+
"eos_token": {
|
17 |
+
"content": "</s>",
|
18 |
+
"lstrip": false,
|
19 |
+
"normalized": false,
|
20 |
+
"rstrip": false,
|
21 |
+
"single_word": false
|
22 |
+
},
|
23 |
+
"mask_token": {
|
24 |
+
"content": "<mask>",
|
25 |
+
"lstrip": true,
|
26 |
+
"normalized": false,
|
27 |
+
"rstrip": false,
|
28 |
+
"single_word": false
|
29 |
+
},
|
30 |
+
"pad_token": {
|
31 |
+
"content": "<pad>",
|
32 |
+
"lstrip": false,
|
33 |
+
"normalized": false,
|
34 |
+
"rstrip": false,
|
35 |
+
"single_word": false
|
36 |
+
},
|
37 |
+
"sep_token": {
|
38 |
+
"content": "</s>",
|
39 |
+
"lstrip": false,
|
40 |
+
"normalized": false,
|
41 |
+
"rstrip": false,
|
42 |
+
"single_word": false
|
43 |
+
},
|
44 |
+
"unk_token": {
|
45 |
+
"content": "[UNK]",
|
46 |
+
"lstrip": false,
|
47 |
+
"normalized": false,
|
48 |
+
"rstrip": false,
|
49 |
+
"single_word": false
|
50 |
+
}
|
51 |
+
}
|
tokenizer.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|
tokenizer_config.json
ADDED
@@ -0,0 +1,59 @@
|
|
|
|
|
|
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|
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|
|
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|
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|
1 |
+
{
|
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"added_tokens_decoder": {
|
3 |
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"0": {
|
4 |
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|
5 |
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|
6 |
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|
7 |
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"rstrip": false,
|
8 |
+
"single_word": false,
|
9 |
+
"special": true
|
10 |
+
},
|
11 |
+
"1": {
|
12 |
+
"content": "<pad>",
|
13 |
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"lstrip": false,
|
14 |
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"normalized": false,
|
15 |
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"rstrip": false,
|
16 |
+
"single_word": false,
|
17 |
+
"special": true
|
18 |
+
},
|
19 |
+
"2": {
|
20 |
+
"content": "</s>",
|
21 |
+
"lstrip": false,
|
22 |
+
"normalized": false,
|
23 |
+
"rstrip": false,
|
24 |
+
"single_word": false,
|
25 |
+
"special": true
|
26 |
+
},
|
27 |
+
"104": {
|
28 |
+
"content": "[UNK]",
|
29 |
+
"lstrip": false,
|
30 |
+
"normalized": false,
|
31 |
+
"rstrip": false,
|
32 |
+
"single_word": false,
|
33 |
+
"special": true
|
34 |
+
},
|
35 |
+
"30526": {
|
36 |
+
"content": "<mask>",
|
37 |
+
"lstrip": true,
|
38 |
+
"normalized": false,
|
39 |
+
"rstrip": false,
|
40 |
+
"single_word": false,
|
41 |
+
"special": true
|
42 |
+
}
|
43 |
+
},
|
44 |
+
"bos_token": "<s>",
|
45 |
+
"clean_up_tokenization_spaces": true,
|
46 |
+
"cls_token": "<s>",
|
47 |
+
"do_basic_tokenize": true,
|
48 |
+
"do_lower_case": true,
|
49 |
+
"eos_token": "</s>",
|
50 |
+
"mask_token": "<mask>",
|
51 |
+
"model_max_length": 512,
|
52 |
+
"never_split": null,
|
53 |
+
"pad_token": "<pad>",
|
54 |
+
"sep_token": "</s>",
|
55 |
+
"strip_accents": null,
|
56 |
+
"tokenize_chinese_chars": true,
|
57 |
+
"tokenizer_class": "MPNetTokenizer",
|
58 |
+
"unk_token": "[UNK]"
|
59 |
+
}
|
vocab.txt
ADDED
The diff for this file is too large to render.
See raw diff
|
|