rafmacalaba
/

lfm2.5-350M-datause-provenance

@@ -1,207 +1,54 @@
 ---
-base_model: LiquidAI/LFM2.5-350M
-library_name: peft
 pipeline_tag: text-generation
 tags:
-- base_model:adapter:LiquidAI/LFM2.5-350M
-- lora
-- transformers
 ---
-# 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. -->
-- **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]
-### Framework versions
-- PEFT 0.20.0

 ---
+license: apache-2.0
 pipeline_tag: text-generation
 tags:
+  - lfm2
+  - data-use
+  - provenance
+  - sft
+  - lora
 ---
+# lfm2.5-350M-datause-provenance
+LoRA SFT of `LiquidAI/LFM2.5-350M` for data-mention provenance attribute extraction
+(producer / year / geography / acronym, verbatim from context).
+## Training
+- base model: `LiquidAI/LFM2.5-350M`
+- dataset: `rafmacalaba/data-use-provenance-sft`
+- epochs: 3
+- learning rate: 0.0002
+- LoRA: r=16 alpha=32 dropout=0.05
+- completion-only masking (loss on assistant JSON turn)
+## Evaluation (holdout, n=3487)
+Exact string match of each emitted attribute against the gold label.
+| attribute | tp | fp | fn | precision | recall | f0.5 | f1 |
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| producer | 1143 | 296 | 306 | 0.7943 | 0.7888 | 0.7932 | 0.7916 |
+| year | 1085 | 207 | 265 | 0.8398 | 0.8037 | 0.8323 | 0.8213 |
+| geography | 1715 | 371 | 312 | 0.8221 | 0.8461 | 0.8268 | 0.8339 |
+| acronym | 1271 | 212 | 145 | 0.8570 | 0.8976 | 0.8649 | 0.8769 |
+| **overall** | 5214 | 1086 | 1028 | 0.8276 | 0.8353 | 0.8291 | 0.8314 |
+Verbatim rate (emitted values that are substrings of the context): 6290/6300 = 0.9984
+## Sample predictions (holdout)
+| mention | target | predicted | correct | verbatim |
+| --- | --- | --- | --- | --- |
+| plant-level data | {"geography": "Indonesian"} | {"geography": "Indonesian"} | geography | geography |
+| GIDD database | {"geography": "all regions in the world"} | {"acronym": "GIDD", "geography": "all regions in the world"} | geography | geography,acronym |
+| NFHS-3 data | {"acronym": "NFHS-3", "geography": "India"} | {"acronym": "NFHS-3", "geography": "India"} | geography,acronym | geography,acronym |
+| data from Hallegatte ( 2012 ) | {"year": "2012"} | {"geography": "LICs", "year": "2012"} | year | year,geography |
+| national accounts | {"geography": "Turkey"} | {"geography": "Turkey"} | geography | geography |
+| SRF registry of firms | {"acronym": "SRF", "geography": "Brazil", "producer": "Brazilian tax authority", "year": "2012"} | {"acronym": "SRF", "geography": "Brazil", "producer": "Brazilian tax authority", "year": "2012"} | producer,year,geography,acronym | producer,year,geography,acronym |
+| Human Capital Index | {"geography": "Kenya", "producer": "World Bank", "year": "2020"} | {"geography": "Kenya", "producer": "World Bank", "year": "2020"} | producer,year,geography | producer,year,geography |
+| United Nations population counts by age | {"producer": "United Nations"} | {"producer": "United Nations"} | producer | producer |
+| 2002 Census | {"geography": "Rwanda", "year": "2002"} | {"geography": "Rwanda", "year": "2002"} | year,geography | year,geography |
+| ENCASEH 1997 | {"acronym": "ENCASEH", "year": "1997"} | {"acronym": "ENCASEH", "year": "1997"} | year,acronym | year,acronym |
+| Agricultural Wages in India | {"acronym": "AWI", "geography": "India", "producer": "Ministry of Agriculture"} | {"acronym": "AWI", "geography": "India", "producer": "Ministry of Agriculture"} | producer,geography,acronym | producer,geography,acronym |
+| COVID-19 Enterprise Survey | {"geography": "Chad", "year": "2020"} | {"acronym": "ES", "geography": "Chad", "year": "2020"} | year,geography | year,geography,acronym |