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Upload fine-tuned model - BLEU: 0.2320

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  1. .gitattributes +6 -0
  2. README.md +4 -48
  3. baseline_results.json +3 -3
  4. checkpoint-11000/config.json +54 -0
  5. checkpoint-11000/generation_config.json +17 -0
  6. checkpoint-11000/model.safetensors +3 -0
  7. checkpoint-11000/optimizer.pt +3 -0
  8. checkpoint-11000/rng_state.pth +3 -0
  9. checkpoint-11000/scaler.pt +3 -0
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  18. checkpoint-11416/config.json +54 -0
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  41. test_results.json +3 -3
  42. train_results.json +3 -3
  43. training_args.bin +1 -1
  44. translation_config.json +3 -3
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README.md CHANGED
@@ -22,21 +22,13 @@ metrics:
22
 
23
  ## Model Description
24
 
25
- This model is a fine-tuned version of `Helsinki-NLP/opus-mt-mul-en` for Swahili-to-English translation, specifically optimized for child helpline call transcriptions in East Africa (Tanzania, Uganda, Kenya).
26
 
27
  **Developed by:** BITZ IT Consulting Ltd
28
  **Project:** OpenCHS (Open Child Helpline System)
29
  **Funded by:** UNICEF Venture Fund
30
  **License:** Apache 2.0
31
 
32
- ## Training Data
33
-
34
- The model was fine-tuned on a combination of:
35
- - NLLB Swahili-English parallel corpus (high quality, weight: 1.0)
36
- - CCAligned web-crawled parallel data (supplementary, weight: 0.7)
37
- - Total training samples: Approximately 50,000+ sentence pairs
38
- - Domain focus: Conversational Swahili from helpline contexts
39
-
40
  ## Performance
41
 
42
  ### Test Set (General Translation)
@@ -49,34 +41,21 @@ The model was fine-tuned on a combination of:
49
  - **Domain chrF:** 2.80
50
  - **Domain COMET-QE:** 0.0000
51
 
52
- *Domain metrics are evaluated on real 10-minute call transcriptions from child helplines.*
53
-
54
  ## Intended Use
55
 
56
- **Primary Use Case:** Translating Swahili helpline call transcriptions to English for:
57
- - Case documentation and reporting
58
- - Quality assurance and supervision
59
- - Cross-border case referrals
60
- - Data analysis and insights
61
 
62
  **Languages:** Swahili (source) → English (target)
63
 
64
- **Limitations:**
65
- - Optimized for conversational Swahili (East African dialects)
66
- - May not perform well on formal/literary Swahili
67
- - Best for text lengths under 512 tokens
68
- - Requires post-editing for critical use cases
69
-
70
  ## Usage
71
 
72
  ```python
73
  from transformers import MarianTokenizer, MarianMTModel
74
 
75
- model_name = "YOUR_USERNAME/brendaogutu/sw-en-translation-v1"
76
  tokenizer = MarianTokenizer.from_pretrained(model_name)
77
  model = MarianMTModel.from_pretrained(model_name)
78
 
79
- # Translate
80
  swahili_text = "Habari za asubuhi. Ninaitwa Amina na nina miaka 14."
81
  inputs = tokenizer(swahili_text, return_tensors="pt", padding=True)
82
  outputs = model.generate(**inputs, num_beams=5, max_length=256)
@@ -88,33 +67,10 @@ print(translation)
88
 
89
  **Base Model:** Helsinki-NLP/opus-mt-mul-en
90
  **Training Epochs:** 8
91
- **Batch Size:** 128 (effective: 128)
92
  **Learning Rate:** 3e-05
93
- **Optimizer:** AdamW
94
  **Hardware:** NVIDIA GPU with FP16 mixed precision
95
 
96
- ## Evaluation Methodology
97
-
98
- 1. **Test Set:** Random 5% split from training distribution
99
- 2. **Domain Evaluation:** Held-out set of real helpline call transcriptions (10-min calls)
100
- 3. **Metrics:** BLEU, chrF, COMET-QE (quality estimation)
101
-
102
- ## Citation
103
-
104
- ```bibtex
105
- @software{openchs_translation_2025,
106
- author = {BITZ IT Consulting Ltd},
107
- title = {Swahili-English Translation Model for OpenCHS},
108
- year = {2025},
109
- publisher = {HuggingFace},
110
- url = {https://huggingface.co/YOUR_USERNAME/brendaogutu/sw-en-translation-v1}
111
- }
112
- ```
113
-
114
- ## Contact
115
-
116
- For questions or issues, please contact: brenda@openchs.org
117
-
118
  ---
119
 
120
  *This model is part of the OpenCHS project supporting child helpline services across East Africa.*
 
22
 
23
  ## Model Description
24
 
25
+ This model is a fine-tuned version of `Helsinki-NLP/opus-mt-mul-en` for Swahili-to-English translation, specifically optimized for child helpline call transcriptions in East Africa.
26
 
27
  **Developed by:** BITZ IT Consulting Ltd
28
  **Project:** OpenCHS (Open Child Helpline System)
29
  **Funded by:** UNICEF Venture Fund
30
  **License:** Apache 2.0
31
 
 
 
 
 
 
 
 
 
32
  ## Performance
33
 
34
  ### Test Set (General Translation)
 
41
  - **Domain chrF:** 2.80
42
  - **Domain COMET-QE:** 0.0000
43
 
 
 
44
  ## Intended Use
45
 
46
+ **Primary Use Case:** Translating Swahili helpline call transcriptions to English for case documentation, quality assurance, and cross-border referrals.
 
 
 
 
47
 
48
  **Languages:** Swahili (source) → English (target)
49
 
 
 
 
 
 
 
50
  ## Usage
51
 
52
  ```python
53
  from transformers import MarianTokenizer, MarianMTModel
54
 
55
+ model_name = "brendaogutu/sw-en-translation-v1"
56
  tokenizer = MarianTokenizer.from_pretrained(model_name)
57
  model = MarianMTModel.from_pretrained(model_name)
58
 
 
59
  swahili_text = "Habari za asubuhi. Ninaitwa Amina na nina miaka 14."
60
  inputs = tokenizer(swahili_text, return_tensors="pt", padding=True)
61
  outputs = model.generate(**inputs, num_beams=5, max_length=256)
 
67
 
68
  **Base Model:** Helsinki-NLP/opus-mt-mul-en
69
  **Training Epochs:** 8
70
+ **Batch Size:** 128
71
  **Learning Rate:** 3e-05
 
72
  **Hardware:** NVIDIA GPU with FP16 mixed precision
73
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
74
  ---
75
 
76
  *This model is part of the OpenCHS project supporting child helpline services across East Africa.*
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