LocalDoc
/

nllb-3.3b-en-az

@@ -1,199 +1,106 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
 ## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
+license: cc-by-4.0
+language:
+- en
+- az
+base_model: facebook/nllb-200-3.3B
+pipeline_tag: translation
+tags:
+- translation
+- azerbaijani
+- nllb
+- english-azerbaijani
 library_name: transformers
+datasets:
+- LocalDoc/english-azerbaijani-parallel-corpus
 ---
+# NLLB-3.3B English→Azerbaijani
+A fine-tuned [NLLB-200-3.3B](https://huggingface.co/facebook/nllb-200-3.3B) model for **English→Azerbaijani** translation. It was trained with LoRA on a ~1.4M-pair corpus combining high-quality distilled translations across multiple domains (formal/encyclopedic, instruction-style, dialogue, and conversational registers), then merged into a self-contained model.
+On a 1,012-sentence FLORES-based benchmark it **outperforms Google Translate and several commercial LLM APIs** on English→Azerbaijani, and approaches the strongest proprietary models — at a fraction of their size and cost.
+## Benchmark Results
+Evaluated on [`LocalDoc/en_az_translate_benchmark`](https://huggingface.co/datasets/LocalDoc/en_az_translate_benchmark) (1,012 sentences, EN→AZ). All metrics are reference-based: chrF++ (`word_order=2`), BLEU (sacreBLEU), and COMET-DA (`Unbabel/wmt22-comet-da`). Higher is better.
+| Model | chrF++ | BLEU | COMET-DA |
+|---|---|---|---|
+| GPT-5.4-mini | 70.08 | 45.61 | 92.86 |
+| Gemini-2.5-flash | 69.61 | 45.71 | 92.70 |
+| **This model (NLLB-3.3B EN→AZ)** | **69.30** | **44.42** | **92.70** |
+| DeepSeek-V4-Pro | 68.67 | 43.88 | 92.78 |
+| DeepSeek-V4-Flash | 67.96 | 42.82 | 92.58 |
+| GPT-4.1 | 67.76 | 43.03 | 92.71 |
+| Google Translate | 66.90 | 41.64 | 92.37 |
+| Gemma-4-31B-it | 66.22 | 40.46 | 92.40 |
+| GPT-5.4-nano | 62.10 | 33.87 | 91.41 |
+| Qwen3.6-35B-A3B | 60.39 | 33.57 | 91.23 |
+| NLLB-200-3.3B (base, zero-shot) | 59.03 | 31.76 | 89.86 |
+Fine-tuning improved the base NLLB-3.3B by **+10.3 chrF++** (59.03 → 69.30) and **+2.84 COMET-DA** (89.86 → 92.70). The result surpasses Google Translate, DeepSeek-V4-Pro, GPT-4.1, Gemma-3-31B, and Qwen-35B, and comes within 0.8 chrF++ of GPT-5.4-mini and Gemini-2.5-flash.
+## Example Translations
+A few cases where this model produces more accurate or more natural Azerbaijani than the base NLLB-3.3B and/or Google Translate. Full sentences shown.
+**EN:** *No worries, take your time. There's really no rush at all.* — idiomatic, not literal
+- **This model:** Narahat olmayın, tələsməyin. Həqiqətən tələsmək lazım deyil.
+- **Google:** Narahat olmayın, tələsməyin. Əslində heç bir tələskənlik yoxdur. — literal and stilted
+**EN:** *Explain like I'm five: how does the internet actually work?* — natural idiom vs. base
+- **This model:** Beş yaşım varmış kimi izah edin: internet əslində necə işləyir?
+- **Base NLLB-3.3B:** Beş yaşında kimi izah edin: İnternet həqiqətən necə işləyir? — *Beş yaşında kimi* is ungrammatical
+## Usage
+```python
+import torch
+from transformers import AutoModelForSeq2SeqLM, NllbTokenizer
+MODEL = "LocalDoc/nllb-3.3b-en-az"
+SRC, TGT = "eng_Latn", "azj_Latn"
+tokenizer = NllbTokenizer.from_pretrained(MODEL, src_lang=SRC, tgt_lang=TGT)
+model = AutoModelForSeq2SeqLM.from_pretrained(MODEL, torch_dtype=torch.bfloat16).eval()
+bos = tokenizer.convert_tokens_to_ids(TGT)
+def translate(texts, num_beams=4, max_length=256):
+    if isinstance(texts, str):
+        texts = [texts]
+    tokenizer.src_lang = SRC
+    enc = tokenizer(texts, return_tensors="pt", padding=True,
+                    truncation=True, max_length=max_length)
+    with torch.no_grad():
+        gen = model.generate(**enc, forced_bos_token_id=bos,
+                             num_beams=num_beams, max_length=max_length)
+    return tokenizer.batch_decode(gen, skip_special_tokens=True)
+print(translate("The agreement is expected to be signed by the end of the month."))
+```
+For best results, translate one sentence at a time (the model is sentence-level). Split long texts into sentences before translating.
 ## Training Details
+- **Base model:** facebook/nllb-200-3.3B
+- **Method:** LoRA (r=32, alpha=64) on attention and FFN projections, then merged
+- **Training data:** ~1.4M EN→AZ pairs, distilled and filtered across domains:
+  - Formal / encyclopedic / news
+  - Instruction-style (assistant tasks, Q&A)
+  - Dialogue and conversational speech
+- **Direction:** English → Azerbaijani (direct, no pivot language)
+- **Sequence length:** 256 tokens
+## Limitations
+- **Sentence-level:** translate sentence by sentence; long documents should be split first.
+- **Direction:** trained for English→Azerbaijani only.
+- **Rare lexical gaps:** very specialized vocabulary (e.g. exotic culinary terms) may occasionally be less precise than large general-purpose systems.
+- **Latin script:** outputs standard literary Azerbaijani in Latin script (`azj_Latn`).
+## Citation
+If you use this model, please cite the LocalDoc organization on Hugging Face.