Add SetFit model
Browse files- 1_Pooling/config.json +2 -1
- README.md +25 -54
- config.json +1 -1
- config_sentence_transformers.json +3 -1
- config_setfit.json +2 -2
- model.safetensors +1 -1
- model_head.pkl +2 -2
1_Pooling/config.json
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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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}
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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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metrics:
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- accuracy
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widget:
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- text: 'first: We recommend
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- text: 'first:
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- text: 'first:
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pipeline_tag: text-classification
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inference: true
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base_model: sentence-transformers/all-MiniLM-L6-v2
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split: test
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metrics:
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- type: accuracy
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value: 0.
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name: Accuracy
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---
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### Metrics
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| Label | Accuracy |
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|:--------|:---------|
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| **all** | 0.
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## Uses
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from setfit import SetFitModel
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# Download from the 🤗 Hub
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model = SetFitModel.from_pretrained("
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# Run inference
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preds = model("first:
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```
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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.
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| 5.0 | 50 | 0.0012 | - |
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| 10.0 | 100 | 0.0006 | - |
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| 0.1 | 1 | 0.0005 | - |
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| 5.0 | 50 | 0.0005 | - |
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| 10.0 | 100 | 0.0002 | - |
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| 15.0 | 150 | 0.0002 | - |
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| 20.0 | 200 | 0.0001 | - |
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| 25.0 | 250 | 0.0001 | - |
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| 30.0 | 300 | 0.0001 | - |
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| 35.0 | 350 | 0.0002 | - |
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| 40.0 | 400 | 0.0 | - |
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| 45.0 | 450 | 0.0 | - |
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| 50.0 | 500 | 0.0 | - |
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| 55.0 | 550 | 0.0 | - |
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| 60.0 | 600 | 0.0 | - |
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| 65.0 | 650 | 0.0001 | - |
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| 70.0 | 700 | 0.0 | - |
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| 75.0 | 750 | 0.0 | - |
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| 80.0 | 800 | 0.0 | - |
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| 85.0 | 850 | 0.0 | - |
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| 90.0 | 900 | 0.0 | - |
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| 95.0 | 950 | 0.0 | - |
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| 100.0 | 1000 | 0.0 | - |
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| 0.0667 | 1 | 0.0 | - |
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| 0.8333 | 50 | 0.0 | - |
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| 0.0222 | 1 | 0.0 | - |
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| 0.8333 | 50 | 0.0 | - |
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| 0.0111 | 1 | 0.0001 | - |
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| 0.5556 | 50 | 0.0 | - |
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| 0.0333 | 1 | 0.0 | - |
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### Framework Versions
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- Python: 3.10.
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- SetFit: 1.0.3
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- Sentence Transformers: 2.
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- Transformers: 4.
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- PyTorch: 2.1
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- Datasets: 2.17.1
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- Tokenizers: 0.15.2
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metrics:
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- accuracy
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widget:
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- text: 'first: We recommend self-help books on conflict resolution, available in
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our office library, as supplemental resources. second: Our company conducts regular
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surveys to identify and address recurring disputes.'
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- text: 'first: Conflict Resolution Apps: We offer technology solutions for reporting
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and tracking conflicts. second: Employees can request a mediator to assist in
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resolving issues with their supervisor, ensuring fair dispute resolution.'
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- text: 'first: Our organization encourages employees to participate in leadership
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development programs, enhancing their ability to interact with supervisors. second:
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Conflict Simulation Exercises: Role-playing helps employees practice resolving
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conflicts.'
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- text: 'first: Mediation sessions are scheduled outside of regular working hours
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for convenience. second: Employee Conflict Coaches: Coaches work one-on-one with
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employees to resolve disputes.'
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- text: 'first: We provide conflict resolution pamphlets in the breakroom, offering
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helpful tips. second: We provide resources for employees to seek external mediation
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or counseling services if disputes with supervisors persist.'
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pipeline_tag: text-classification
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inference: true
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base_model: sentence-transformers/all-MiniLM-L6-v2
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split: test
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metrics:
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- type: accuracy
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value: 0.4090909090909091
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name: Accuracy
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---
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### Metrics
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| Label | Accuracy |
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|:--------|:---------|
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| **all** | 0.4091 |
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## Uses
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from setfit import SetFitModel
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# Download from the 🤗 Hub
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model = SetFitModel.from_pretrained("diegofiggie/fairness_model")
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# Run inference
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preds = model("first: Mediation sessions are scheduled outside of regular working hours for convenience. second: Employee Conflict Coaches: Coaches work one-on-one with employees to resolve disputes.")
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```
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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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| 0.0333 | 1 | 0.2322 | - |
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### Framework Versions
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- Python: 3.10.9
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- SetFit: 1.0.3
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- Sentence Transformers: 2.4.0
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- Transformers: 4.38.1
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- PyTorch: 2.2.1+cpu
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- Datasets: 2.17.1
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- Tokenizers: 0.15.2
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config.json
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 30522
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.38.1",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 30522
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config_sentence_transformers.json
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"sentence_transformers": "2.0.0",
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"transformers": "4.6.1",
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"pytorch": "1.8.1"
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}
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}
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"sentence_transformers": "2.0.0",
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"transformers": "4.6.1",
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"pytorch": "1.8.1"
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},
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"prompts": {},
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"default_prompt_name": null
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}
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config_setfit.json
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{
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"
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}
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{
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"normalize_embeddings": false,
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"labels": null
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 90864192
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version https://git-lfs.github.com/spec/v1
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oid sha256:271165e1148d41d5f71a15902a0759c4515a26e700c5068405f4db54cdcdfde6
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size 90864192
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model_head.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:2ca976370557f01d234bcfba600462a5c37a6308e8eed07b33f09fe60ca0a181
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size 3813
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