thegenerativegeneration
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Browse files- .gitattributes +1 -0
- 1_Pooling/config.json +10 -0
- README.md +387 -0
- config.json +26 -0
- config_sentence_transformers.json +9 -0
- config_setfit.json +4 -0
- model.safetensors +3 -0
- model_head.pkl +3 -0
- modules.json +20 -0
- sentence_bert_config.json +4 -0
- sentencepiece.bpe.model +3 -0
- special_tokens_map.json +51 -0
- tokenizer.json +3 -0
- tokenizer_config.json +62 -0
.gitattributes
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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1_Pooling/config.json
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{
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"word_embedding_dimension": 384,
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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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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}
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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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- sentence-transformers
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- text-classification
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- generated_from_setfit_trainer
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base_model: intfloat/multilingual-e5-small
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metrics:
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- accuracy
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widget:
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- text: 'query: Ναι, ας πάμε!'
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- text: 'query: 信じられない!それはすごいね。熊を見たなんて!'
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- text: 'query: Taky dobře. Potkáme se zítra?'
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- text: 'query: Sì, mi farebbe piacere. A tra poco!'
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- text: 'query: Γεια σου, πώς είσαι;'
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pipeline_tag: text-classification
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inference: true
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---
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# SetFit with intfloat/multilingual-e5-small
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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 [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance is used for classification.
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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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## Model Details
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### Model Description
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- **Model Type:** SetFit
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- **Sentence Transformer body:** [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small)
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- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
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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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### Model Sources
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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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| 1 | <ul><li>'query: ਚੰਗਾ ਜੀ, ਫਿਰ ਮਿਲਦੇ ਹਾਂ.'</li><li>'query: Agur, gero arte.'</li><li>"query: Me'n vaig ara."</li></ul> |
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| 0 | <ul><li>'query: Dobro, hvala. Kaj pa ti?'</li><li>'query: हाँ अगली बार जब तुम जाओ मुझे भी ले चलो मुझे भी प्रकृति में और गतिविधियाँ करनी हैं'</li><li>'query: Mirë, faleminderit. Po ju?'</li></ul> |
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+
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+
## Uses
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### Direct Use for Inference
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+
First install the SetFit library:
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```bash
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pip install setfit
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```
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Then you can load this model and run inference.
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65 |
+
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66 |
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```python
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from setfit import SetFitModel
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# Download from the 🤗 Hub
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model = SetFitModel.from_pretrained("setfit_model_id")
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# Run inference
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preds = model("query: Ναι, ας πάμε!")
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```
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<!--
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### Downstream Use
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77 |
+
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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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83 |
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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
|
95 |
+
|
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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 | 2 | 7.4364 | 21 |
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| Label | Training Sample Count |
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|:------|:----------------------|
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| 0 | 292 |
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| 1 | 290 |
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### Training Hyperparameters
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- batch_size: (16, 2)
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- num_epochs: (1, 16)
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- max_steps: -1
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- sampling_strategy: undersampling
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- body_learning_rate: (1e-05, 1e-05)
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- head_learning_rate: 0.001
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- loss: CosineSimilarityLoss
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- distance_metric: cosine_distance
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- margin: 0.1
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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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- run_name: intfloat/multilingual-e5-small
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- eval_max_steps: -1
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- load_best_model_at_end: True
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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 | 0.3645 | - |
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| 0.0047 | 50 | 0.3527 | - |
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| 0.0094 | 100 | 0.3424 | 0.3165 |
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| 0.0142 | 150 | 0.3108 | - |
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| 0.0189 | 200 | 0.2684 | 0.2215 |
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| 0.0236 | 250 | 0.2197 | - |
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| 0.0283 | 300 | 0.1707 | 0.1792 |
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| 0.0331 | 350 | 0.1501 | - |
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| 0.0378 | 400 | 0.0865 | 0.1607 |
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| 0.0425 | 450 | 0.0534 | - |
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| 0.0472 | 500 | 0.0307 | 0.1519 |
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| 0.0520 | 550 | 0.0342 | - |
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| 0.0567 | 600 | 0.0078 | 0.1478 |
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| 0.0614 | 650 | 0.0144 | - |
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| 0.0661 | 700 | 0.0658 | 0.1399 |
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| 0.0709 | 750 | 0.0021 | - |
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| 0.0756 | 800 | 0.0009 | 0.1512 |
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| 0.0803 | 850 | 0.0005 | - |
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| 0.0850 | 900 | 0.0018 | 0.1516 |
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| 0.0897 | 950 | 0.0011 | - |
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| 0.0945 | 1000 | 0.0012 | 0.1541 |
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| 0.0992 | 1050 | 0.0003 | - |
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| 0.1039 | 1100 | 0.0003 | 0.1415 |
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| 0.1086 | 1150 | 0.0003 | - |
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| 0.1134 | 1200 | 0.0002 | 0.1442 |
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| 0.1181 | 1250 | 0.0006 | - |
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| 0.1228 | 1300 | 0.0002 | 0.1298 |
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| 0.1275 | 1350 | 0.0002 | - |
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| 0.1323 | 1400 | 0.0001 | 0.1356 |
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| 0.1370 | 1450 | 0.0002 | - |
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| 0.1417 | 1500 | 0.0003 | 0.1493 |
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| 0.1464 | 1550 | 0.0003 | - |
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| 0.1512 | 1600 | 0.0002 | 0.15 |
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| 0.1559 | 1650 | 0.0002 | - |
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| 0.1606 | 1700 | 0.0003 | 0.1469 |
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| 0.1653 | 1750 | 0.0001 | - |
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| 0.1701 | 1800 | 0.0001 | 0.1554 |
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| 0.1748 | 1850 | 0.0002 | - |
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| 0.1795 | 1900 | 0.0001 | 0.168 |
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| 0.1842 | 1950 | 0.0001 | - |
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| 0.1889 | 2000 | 0.0004 | 0.1568 |
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| 0.1937 | 2050 | 0.0001 | - |
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| 0.1984 | 2100 | 0.0001 | 0.1513 |
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| 0.2031 | 2150 | 0.0001 | - |
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| 0.2078 | 2200 | 0.0003 | 0.1503 |
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| 0.2126 | 2250 | 0.0002 | - |
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| 0.2173 | 2300 | 0.0604 | 0.155 |
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| 0.2220 | 2350 | 0.0001 | - |
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| 0.2267 | 2400 | 0.0002 | 0.1739 |
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| 0.2315 | 2450 | 0.0006 | - |
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| 0.2362 | 2500 | 0.0002 | 0.1558 |
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| 0.2409 | 2550 | 0.0002 | - |
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| 0.2456 | 2600 | 0.0001 | 0.1393 |
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| 0.2504 | 2650 | 0.0004 | - |
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| 0.2551 | 2700 | 0.0003 | 0.1642 |
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| 0.2598 | 2750 | 0.0002 | - |
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| 0.2645 | 2800 | 0.0002 | 0.1776 |
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| 0.2692 | 2850 | 0.0 | - |
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| 0.2740 | 2900 | 0.0002 | 0.1794 |
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| 0.2787 | 2950 | 0.0001 | - |
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| 0.2834 | 3000 | 0.0001 | 0.183 |
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| 0.2881 | 3050 | 0.0001 | - |
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| 0.2929 | 3100 | 0.0001 | 0.1805 |
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| 0.2976 | 3150 | 0.0001 | - |
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| 0.3023 | 3200 | 0.0001 | 0.1757 |
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| 0.3070 | 3250 | 0.0001 | - |
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| 0.3118 | 3300 | 0.0001 | 0.1302 |
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| 0.3165 | 3350 | 0.0001 | - |
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| 0.3212 | 3400 | 0.0001 | 0.1348 |
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| 0.3259 | 3450 | 0.0001 | - |
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| 0.3307 | 3500 | 0.0005 | 0.1623 |
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| 0.3354 | 3550 | 0.0 | - |
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| 0.3401 | 3600 | 0.0 | 0.1286 |
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| 0.3448 | 3650 | 0.0 | - |
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| 0.3496 | 3700 | 0.0001 | 0.1736 |
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| 0.3543 | 3750 | 0.0 | - |
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| 0.3590 | 3800 | 0.0 | 0.127 |
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| 0.3637 | 3850 | 0.0 | - |
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| 0.3684 | 3900 | 0.0001 | 0.1231 |
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| 0.3732 | 3950 | 0.0 | - |
|
212 |
+
| **0.3779** | **4000** | **0.0001** | **0.1261** |
|
213 |
+
| 0.3826 | 4050 | 0.0001 | - |
|
214 |
+
| 0.3873 | 4100 | 0.0 | 0.1216 |
|
215 |
+
| 0.3921 | 4150 | 0.0 | - |
|
216 |
+
| 0.3968 | 4200 | 0.0 | 0.1404 |
|
217 |
+
| 0.4015 | 4250 | 0.0 | - |
|
218 |
+
| 0.4062 | 4300 | 0.0 | 0.1466 |
|
219 |
+
| 0.4110 | 4350 | 0.0 | - |
|
220 |
+
| 0.4157 | 4400 | 0.0 | 0.1482 |
|
221 |
+
| 0.4204 | 4450 | 0.0 | - |
|
222 |
+
| 0.4251 | 4500 | 0.0 | 0.1547 |
|
223 |
+
| 0.4299 | 4550 | 0.0 | - |
|
224 |
+
| 0.4346 | 4600 | 0.0 | 0.1566 |
|
225 |
+
| 0.4393 | 4650 | 0.0 | - |
|
226 |
+
| 0.4440 | 4700 | 0.0 | 0.1684 |
|
227 |
+
| 0.4487 | 4750 | 0.0 | - |
|
228 |
+
| 0.4535 | 4800 | 0.0 | 0.1746 |
|
229 |
+
| 0.4582 | 4850 | 0.0 | - |
|
230 |
+
| 0.4629 | 4900 | 0.0 | 0.167 |
|
231 |
+
| 0.4676 | 4950 | 0.0 | - |
|
232 |
+
| 0.4724 | 5000 | 0.0001 | 0.1683 |
|
233 |
+
| 0.4771 | 5050 | 0.0 | - |
|
234 |
+
| 0.4818 | 5100 | 0.0 | 0.1693 |
|
235 |
+
| 0.4865 | 5150 | 0.0 | - |
|
236 |
+
| 0.4913 | 5200 | 0.0 | 0.1694 |
|
237 |
+
| 0.4960 | 5250 | 0.0 | - |
|
238 |
+
| 0.5007 | 5300 | 0.0 | 0.162 |
|
239 |
+
| 0.5054 | 5350 | 0.0 | - |
|
240 |
+
| 0.5102 | 5400 | 0.0 | 0.1388 |
|
241 |
+
| 0.5149 | 5450 | 0.0 | - |
|
242 |
+
| 0.5196 | 5500 | 0.0 | 0.1353 |
|
243 |
+
| 0.5243 | 5550 | 0.0 | - |
|
244 |
+
| 0.5291 | 5600 | 0.0 | 0.1401 |
|
245 |
+
| 0.5338 | 5650 | 0.0 | - |
|
246 |
+
| 0.5385 | 5700 | 0.0 | 0.1466 |
|
247 |
+
| 0.5432 | 5750 | 0.0 | - |
|
248 |
+
| 0.5479 | 5800 | 0.0 | 0.1529 |
|
249 |
+
| 0.5527 | 5850 | 0.0 | - |
|
250 |
+
| 0.5574 | 5900 | 0.0 | 0.1488 |
|
251 |
+
| 0.5621 | 5950 | 0.0 | - |
|
252 |
+
| 0.5668 | 6000 | 0.0 | 0.147 |
|
253 |
+
| 0.5716 | 6050 | 0.0 | - |
|
254 |
+
| 0.5763 | 6100 | 0.0 | 0.1493 |
|
255 |
+
| 0.5810 | 6150 | 0.0 | - |
|
256 |
+
| 0.5857 | 6200 | 0.0 | 0.1525 |
|
257 |
+
| 0.5905 | 6250 | 0.0 | - |
|
258 |
+
| 0.5952 | 6300 | 0.0 | 0.1505 |
|
259 |
+
| 0.5999 | 6350 | 0.0 | - |
|
260 |
+
| 0.6046 | 6400 | 0.0 | 0.1554 |
|
261 |
+
| 0.6094 | 6450 | 0.0 | - |
|
262 |
+
| 0.6141 | 6500 | 0.0 | 0.1546 |
|
263 |
+
| 0.6188 | 6550 | 0.0 | - |
|
264 |
+
| 0.6235 | 6600 | 0.0 | 0.1598 |
|
265 |
+
| 0.6282 | 6650 | 0.0 | - |
|
266 |
+
| 0.6330 | 6700 | 0.0 | 0.179 |
|
267 |
+
| 0.6377 | 6750 | 0.0 | - |
|
268 |
+
| 0.6424 | 6800 | 0.0 | 0.1719 |
|
269 |
+
| 0.6471 | 6850 | 0.0001 | - |
|
270 |
+
| 0.6519 | 6900 | 0.0 | 0.1812 |
|
271 |
+
| 0.6566 | 6950 | 0.0 | - |
|
272 |
+
| 0.6613 | 7000 | 0.0 | 0.1648 |
|
273 |
+
| 0.6660 | 7050 | 0.0 | - |
|
274 |
+
| 0.6708 | 7100 | 0.0 | 0.1717 |
|
275 |
+
| 0.6755 | 7150 | 0.0 | - |
|
276 |
+
| 0.6802 | 7200 | 0.0 | 0.1793 |
|
277 |
+
| 0.6849 | 7250 | 0.0 | - |
|
278 |
+
| 0.6897 | 7300 | 0.0 | 0.1766 |
|
279 |
+
| 0.6944 | 7350 | 0.0 | - |
|
280 |
+
| 0.6991 | 7400 | 0.0 | 0.177 |
|
281 |
+
| 0.7038 | 7450 | 0.0 | - |
|
282 |
+
| 0.7085 | 7500 | 0.0 | 0.1749 |
|
283 |
+
| 0.7133 | 7550 | 0.0 | - |
|
284 |
+
| 0.7180 | 7600 | 0.0 | 0.1814 |
|
285 |
+
| 0.7227 | 7650 | 0.0 | - |
|
286 |
+
| 0.7274 | 7700 | 0.0 | 0.1742 |
|
287 |
+
| 0.7322 | 7750 | 0.0 | - |
|
288 |
+
| 0.7369 | 7800 | 0.0 | 0.179 |
|
289 |
+
| 0.7416 | 7850 | 0.0 | - |
|
290 |
+
| 0.7463 | 7900 | 0.0 | 0.1767 |
|
291 |
+
| 0.7511 | 7950 | 0.0 | - |
|
292 |
+
| 0.7558 | 8000 | 0.0 | 0.1809 |
|
293 |
+
| 0.7605 | 8050 | 0.0 | - |
|
294 |
+
| 0.7652 | 8100 | 0.0 | 0.1767 |
|
295 |
+
| 0.7700 | 8150 | 0.0 | - |
|
296 |
+
| 0.7747 | 8200 | 0.0 | 0.1698 |
|
297 |
+
| 0.7794 | 8250 | 0.0 | - |
|
298 |
+
| 0.7841 | 8300 | 0.0 | 0.1772 |
|
299 |
+
| 0.7889 | 8350 | 0.0 | - |
|
300 |
+
| 0.7936 | 8400 | 0.0 | 0.1722 |
|
301 |
+
| 0.7983 | 8450 | 0.0 | - |
|
302 |
+
| 0.8030 | 8500 | 0.0 | 0.1671 |
|
303 |
+
| 0.8077 | 8550 | 0.0 | - |
|
304 |
+
| 0.8125 | 8600 | 0.0 | 0.181 |
|
305 |
+
| 0.8172 | 8650 | 0.0 | - |
|
306 |
+
| 0.8219 | 8700 | 0.0 | 0.1788 |
|
307 |
+
| 0.8266 | 8750 | 0.0 | - |
|
308 |
+
| 0.8314 | 8800 | 0.0 | 0.1784 |
|
309 |
+
| 0.8361 | 8850 | 0.0 | - |
|
310 |
+
| 0.8408 | 8900 | 0.0 | 0.1806 |
|
311 |
+
| 0.8455 | 8950 | 0.0 | - |
|
312 |
+
| 0.8503 | 9000 | 0.0 | 0.1783 |
|
313 |
+
| 0.8550 | 9050 | 0.0 | - |
|
314 |
+
| 0.8597 | 9100 | 0.0 | 0.1783 |
|
315 |
+
| 0.8644 | 9150 | 0.0 | - |
|
316 |
+
| 0.8692 | 9200 | 0.0 | 0.1785 |
|
317 |
+
| 0.8739 | 9250 | 0.0 | - |
|
318 |
+
| 0.8786 | 9300 | 0.0 | 0.1772 |
|
319 |
+
| 0.8833 | 9350 | 0.0 | - |
|
320 |
+
| 0.8880 | 9400 | 0.0 | 0.1816 |
|
321 |
+
| 0.8928 | 9450 | 0.0 | - |
|
322 |
+
| 0.8975 | 9500 | 0.0 | 0.1794 |
|
323 |
+
| 0.9022 | 9550 | 0.0 | - |
|
324 |
+
| 0.9069 | 9600 | 0.0 | 0.168 |
|
325 |
+
| 0.9117 | 9650 | 0.0 | - |
|
326 |
+
| 0.9164 | 9700 | 0.0 | 0.1771 |
|
327 |
+
| 0.9211 | 9750 | 0.0 | - |
|
328 |
+
| 0.9258 | 9800 | 0.0 | 0.1675 |
|
329 |
+
| 0.9306 | 9850 | 0.0 | - |
|
330 |
+
| 0.9353 | 9900 | 0.0 | 0.1746 |
|
331 |
+
| 0.9400 | 9950 | 0.0 | - |
|
332 |
+
| 0.9447 | 10000 | 0.0 | 0.1769 |
|
333 |
+
| 0.9495 | 10050 | 0.0 | - |
|
334 |
+
| 0.9542 | 10100 | 0.0 | 0.177 |
|
335 |
+
| 0.9589 | 10150 | 0.0 | - |
|
336 |
+
| 0.9636 | 10200 | 0.0 | 0.1771 |
|
337 |
+
| 0.9684 | 10250 | 0.0 | - |
|
338 |
+
| 0.9731 | 10300 | 0.0 | 0.1794 |
|
339 |
+
| 0.9778 | 10350 | 0.0 | - |
|
340 |
+
| 0.9825 | 10400 | 0.0 | 0.177 |
|
341 |
+
| 0.9872 | 10450 | 0.0 | - |
|
342 |
+
| 0.9920 | 10500 | 0.0 | 0.1794 |
|
343 |
+
| 0.9967 | 10550 | 0.0 | - |
|
344 |
+
|
345 |
+
* The bold row denotes the saved checkpoint.
|
346 |
+
### Framework Versions
|
347 |
+
- Python: 3.10.11
|
348 |
+
- SetFit: 1.0.3
|
349 |
+
- Sentence Transformers: 2.7.0
|
350 |
+
- Transformers: 4.39.0
|
351 |
+
- PyTorch: 2.3.1
|
352 |
+
- Datasets: 2.20.0
|
353 |
+
- Tokenizers: 0.15.2
|
354 |
+
|
355 |
+
## Citation
|
356 |
+
|
357 |
+
### BibTeX
|
358 |
+
```bibtex
|
359 |
+
@article{https://doi.org/10.48550/arxiv.2209.11055,
|
360 |
+
doi = {10.48550/ARXIV.2209.11055},
|
361 |
+
url = {https://arxiv.org/abs/2209.11055},
|
362 |
+
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
|
363 |
+
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
|
364 |
+
title = {Efficient Few-Shot Learning Without Prompts},
|
365 |
+
publisher = {arXiv},
|
366 |
+
year = {2022},
|
367 |
+
copyright = {Creative Commons Attribution 4.0 International}
|
368 |
+
}
|
369 |
+
```
|
370 |
+
|
371 |
+
<!--
|
372 |
+
## Glossary
|
373 |
+
|
374 |
+
*Clearly define terms in order to be accessible across audiences.*
|
375 |
+
-->
|
376 |
+
|
377 |
+
<!--
|
378 |
+
## Model Card Authors
|
379 |
+
|
380 |
+
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
381 |
+
-->
|
382 |
+
|
383 |
+
<!--
|
384 |
+
## Model Card Contact
|
385 |
+
|
386 |
+
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
|
387 |
+
-->
|
config.json
ADDED
@@ -0,0 +1,26 @@
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
1 |
+
{
|
2 |
+
"_name_or_path": "checkpoints/step_4000",
|
3 |
+
"architectures": [
|
4 |
+
"BertModel"
|
5 |
+
],
|
6 |
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"attention_probs_dropout_prob": 0.1,
|
7 |
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"classifier_dropout": null,
|
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"hidden_act": "gelu",
|
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"hidden_dropout_prob": 0.1,
|
10 |
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"hidden_size": 384,
|
11 |
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"initializer_range": 0.02,
|
12 |
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"intermediate_size": 1536,
|
13 |
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"layer_norm_eps": 1e-12,
|
14 |
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"max_position_embeddings": 512,
|
15 |
+
"model_type": "bert",
|
16 |
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"num_attention_heads": 12,
|
17 |
+
"num_hidden_layers": 12,
|
18 |
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"pad_token_id": 0,
|
19 |
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"position_embedding_type": "absolute",
|
20 |
+
"tokenizer_class": "XLMRobertaTokenizer",
|
21 |
+
"torch_dtype": "float32",
|
22 |
+
"transformers_version": "4.39.0",
|
23 |
+
"type_vocab_size": 2,
|
24 |
+
"use_cache": true,
|
25 |
+
"vocab_size": 250037
|
26 |
+
}
|
config_sentence_transformers.json
ADDED
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"__version__": {
|
3 |
+
"sentence_transformers": "2.7.0",
|
4 |
+
"transformers": "4.39.0",
|
5 |
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"pytorch": "2.3.1"
|
6 |
+
},
|
7 |
+
"prompts": {},
|
8 |
+
"default_prompt_name": null
|
9 |
+
}
|
config_setfit.json
ADDED
@@ -0,0 +1,4 @@
|
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|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
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"normalize_embeddings": false,
|
3 |
+
"labels": null
|
4 |
+
}
|
model.safetensors
ADDED
@@ -0,0 +1,3 @@
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|
1 |
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version https://git-lfs.github.com/spec/v1
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2 |
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oid sha256:f2b5a6014e84745ed333637493e2041576d65b00357db523349592123df37b3f
|
3 |
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size 470637416
|
model_head.pkl
ADDED
@@ -0,0 +1,3 @@
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|
1 |
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version https://git-lfs.github.com/spec/v1
|
2 |
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oid sha256:6dc7d3f2457dc9c7cc2674e086e153cc12a1476b7732b381b2c9d9ed43c16543
|
3 |
+
size 4608
|
modules.json
ADDED
@@ -0,0 +1,20 @@
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
[
|
2 |
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{
|
3 |
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"idx": 0,
|
4 |
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"name": "0",
|
5 |
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"path": "",
|
6 |
+
"type": "sentence_transformers.models.Transformer"
|
7 |
+
},
|
8 |
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{
|
9 |
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"idx": 1,
|
10 |
+
"name": "1",
|
11 |
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"path": "1_Pooling",
|
12 |
+
"type": "sentence_transformers.models.Pooling"
|
13 |
+
},
|
14 |
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{
|
15 |
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"idx": 2,
|
16 |
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"name": "2",
|
17 |
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"path": "2_Normalize",
|
18 |
+
"type": "sentence_transformers.models.Normalize"
|
19 |
+
}
|
20 |
+
]
|
sentence_bert_config.json
ADDED
@@ -0,0 +1,4 @@
|
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|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"max_seq_length": 512,
|
3 |
+
"do_lower_case": false
|
4 |
+
}
|
sentencepiece.bpe.model
ADDED
@@ -0,0 +1,3 @@
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1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:cfc8146abe2a0488e9e2a0c56de7952f7c11ab059eca145a0a727afce0db2865
|
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size 5069051
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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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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
29 |
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|
30 |
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|
31 |
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|
32 |
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|
33 |
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|
34 |
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|
35 |
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|
36 |
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|
37 |
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|
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|
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|
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|
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|
42 |
+
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|
43 |
+
},
|
44 |
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|
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|
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|
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|
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|
49 |
+
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|
50 |
+
}
|
51 |
+
}
|
tokenizer.json
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
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|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
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oid sha256:55ce1a4600af70b33f5a7fba12dbb41a504d3c08737c9b26b5e7fd6e437a9a23
|
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size 17083087
|
tokenizer_config.json
ADDED
@@ -0,0 +1,62 @@
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{
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"added_tokens_decoder": {
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"0": {
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"content": "<s>",
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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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"1": {
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"content": "<pad>",
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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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"2": {
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"content": "</s>",
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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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"3": {
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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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"250001": {
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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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"bos_token": "<s>",
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"clean_up_tokenization_spaces": true,
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"cls_token": "<s>",
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"eos_token": "</s>",
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"mask_token": "<mask>",
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"max_length": 512,
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"model_max_length": 512,
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"pad_to_multiple_of": null,
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"pad_token": "<pad>",
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"pad_token_type_id": 0,
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"padding_side": "right",
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"sep_token": "</s>",
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"sp_model_kwargs": {},
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"stride": 0,
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"tokenizer_class": "XLMRobertaTokenizer",
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"truncation_side": "right",
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"truncation_strategy": "longest_first",
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"unk_token": "<unk>"
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}
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