Add SetFit model
Browse files- README.md +55 -57
- model_head.pkl +1 -1
- pytorch_model.bin +1 -1
README.md
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@@ -15,37 +15,35 @@ metrics:
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- F1-Score
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- accuracy
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widget:
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- text:
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of the population was found in a situation of monetary poverty (41.4% of the population
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in the case of the villages and rural centers scattered).
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pipeline_tag: text-classification
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inference: false
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base_model: sentence-transformers/all-mpnet-base-v2
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split: test
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metrics:
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- type: Precision_micro
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value: 0.
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name: Precision_Micro
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- type: Precision_weighted
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value: 0.
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name: Precision_Weighted
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- type: Precision_samples
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value: 0.
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name: Precision_Samples
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- type: Recall_micro
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value: 0.
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name: Recall_Micro
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- type: Recall_weighted
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value: 0.
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name: Recall_Weighted
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- type: Recall_samples
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value: 0.
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name: Recall_Samples
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- type: F1-Score
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value: 0.
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name: F1-Score
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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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@@ -118,7 +116,7 @@ The model has been trained using an efficient few-shot learning technique that i
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### Metrics
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| Label | Precision_Micro | Precision_Weighted | Precision_Samples | Recall_Micro | Recall_Weighted | Recall_Samples | F1-Score | Accuracy |
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|:--------|:----------------|:-------------------|:------------------|:-------------|:----------------|:---------------|:---------|:---------|
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| **all** | 0.
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## Uses
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@@ -138,7 +136,7 @@ from setfit import SetFitModel
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# Download from the 🤗 Hub
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model = SetFitModel.from_pretrained("leavoigt/vulnerability_target")
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# Run inference
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preds = model("
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```
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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 | 15 |
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### Training Hyperparameters
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- batch_size: (16, 16)
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### Training Results
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| Epoch | Step | Training Loss | Validation Loss |
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|:------:|:----:|:-------------:|:---------------:|
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| 0.0012 | 1 | 0.
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| 0.0602 | 50 | 0.
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| 0.1205 | 100 | 0.
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| 0.1807 | 150 | 0.
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| 0.2410 | 200 | 0.
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| 0.3012 | 250 | 0.
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| 0.3614 | 300 | 0.
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| 0.4217 | 350 | 0.
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| 0.4819 | 400 | 0.
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| 0.5422 | 450 | 0.
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| 0.6024 | 500 | 0.0002 | - |
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| 0.6627 | 550 | 0.
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| 0.7229 | 600 | 0.
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| 0.7831 | 650 | 0.
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| 0.8434 | 700 | 0.
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| 0.9036 | 750 | 0.0003 | - |
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| 0.9639 | 800 | 0.
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### Framework Versions
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- Python: 3.10.12
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- F1-Score
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- accuracy
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widget:
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- text: Amended proposal for a Regulation of the European Parliament and of the Council
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on establishing the framework for achieving climate neutrality and amending Regulation
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(EU) 2018/1999 (European Climate Law). COM(2020) 563 (currently undergoing the
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EU internal legislative process)↩︎. Council conclusions of 7 March 2011 on European
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Pact for Gender Equality (2011-2020)↩︎. Council conclusions of 9 April 2019, Towards
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an ever more sustainable Union by 2030↩︎. Council conclusions of 15 May 2017 on
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Indigenous Peoples↩︎. Regulation (EU) 2018/1999↩︎.
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- text: 'Development of 15,000 ha of shallows and irrigated areas and their exploitation
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for the intensive rice cultivation system. Agriculture, water. 705. 28. Development
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of research on health and climate change: total of three activities. Health. 690.
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29. Audit of plans to develop all classified or protected forests for updating
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purposes. Forests-land use. 685. 30. Strengthening of capabilities to forecast
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and respond to phenomena associated with climate change: creation of an MT health
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care monitoring centre. Health. 680. 31. Participative development of sustainable
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land.'
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- text: The Ministry of Health notes that any adaptation work should prioritise vulnerable
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populations. It also considers that more work is needed in health system planning,
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to accommodate a potential increase in migrants and refugees
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- text: 'The overall outcome is to ensure that projects and programmes are gender
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responsive: meaning that it aims to go beyond gender sensitivity to actively promote
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gender equality and women’s empowerment. The country is committed to achieving
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SDG 5: Gender equality by promoting low carbon development where men and women
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contributions to climate change mitigation and adaptation are recognized and valued,
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existing gender inequalities are reduced and opportunities for effective empowerment
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for women are promoted.'
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- text: Cities depend heavily on other cities and regions to provide them with indispensable
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services such as food, water and energy and the infrastructure to deliver them.
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Ecosystem services from surrounding regions provide fresh air, store or drain
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flood water as well as drinking water
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pipeline_tag: text-classification
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inference: false
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base_model: sentence-transformers/all-mpnet-base-v2
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split: test
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metrics:
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- type: Precision_micro
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value: 0.7692307692307693
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name: Precision_Micro
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- type: Precision_weighted
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value: 0.7748199704721445
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name: Precision_Weighted
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- type: Precision_samples
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value: 0.7692307692307693
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name: Precision_Samples
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- type: Recall_micro
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value: 0.7692307692307693
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name: Recall_Micro
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- type: Recall_weighted
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value: 0.7692307692307693
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name: Recall_Weighted
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- type: Recall_samples
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value: 0.7692307692307693
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name: Recall_Samples
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- type: F1-Score
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value: 0.7692307692307693
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name: F1-Score
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- type: accuracy
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value: 0.7692307692307693
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name: Accuracy
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---
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### Metrics
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| Label | Precision_Micro | Precision_Weighted | Precision_Samples | Recall_Micro | Recall_Weighted | Recall_Samples | F1-Score | Accuracy |
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|:--------|:----------------|:-------------------|:------------------|:-------------|:----------------|:---------------|:---------|:---------|
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| **all** | 0.7692 | 0.7748 | 0.7692 | 0.7692 | 0.7692 | 0.7692 | 0.7692 | 0.7692 |
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## Uses
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# Download from the 🤗 Hub
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model = SetFitModel.from_pretrained("leavoigt/vulnerability_target")
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# Run inference
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preds = model("The Ministry of Health notes that any adaptation work should prioritise vulnerable populations. It also considers that more work is needed in health system planning, to accommodate a potential increase in migrants and refugees")
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```
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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 | 15 | 72.4819 | 238 |
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### Training Hyperparameters
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- batch_size: (16, 16)
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### Training Results
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| Epoch | Step | Training Loss | Validation Loss |
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|:------:|:----:|:-------------:|:---------------:|
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| 0.0012 | 1 | 0.2938 | - |
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| 0.0602 | 50 | 0.2188 | - |
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| 0.1205 | 100 | 0.1733 | - |
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| 0.1807 | 150 | 0.1578 | - |
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| 0.2410 | 200 | 0.02 | - |
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| 0.3012 | 250 | 0.0028 | - |
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| 0.3614 | 300 | 0.0004 | - |
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| 0.4217 | 350 | 0.0011 | - |
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| 0.4819 | 400 | 0.0008 | - |
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| 0.5422 | 450 | 0.0005 | - |
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| 0.6024 | 500 | 0.0002 | - |
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| 0.6627 | 550 | 0.0002 | - |
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| 0.7229 | 600 | 0.0004 | - |
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| 0.7831 | 650 | 0.0332 | - |
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| 0.8434 | 700 | 0.0003 | - |
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| 0.9036 | 750 | 0.0003 | - |
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| 0.9639 | 800 | 0.0004 | - |
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### Framework Versions
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- Python: 3.10.12
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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 13956
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size 13956
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pytorch_model.bin
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