Improve public model card for launch

README.md

@@ -1,8 +1,12 @@
 ---
 license: apache-2.0
+library_name: transformers
+pipeline_tag: text-classification
 base_model: FacebookAI/roberta-large
 datasets:
 - google-research-datasets/go_emotions
+metrics:
+- f1
 language:
 - en
 tags:
@@ -12,87 +16,113 @@ tags:
 - roberta
 - focal-loss
 - threshold-optimization
-metrics:
-- f1
-pipeline_tag: text-classification
-library_name: transformers
+- sota-level
 ---
 
-# GoEmotions RoBERTa Large Focal SOTA
+# GoEmotions RoBERTa-large Focal SOTA-Level Classifier
 
-RoBERTa-large multi-label classifier fine-tuned on the public GoEmotions
-`simplified` split. The model predicts 28 labels: 27 fine-grained emotions plus
-`neutral`.
+This model is a RoBERTa-large multi-label emotion classifier trained on the
+public GoEmotions simplified split. It predicts 27 fine-grained emotions plus
+`neutral` from English Reddit-style text.
 
-## Results
+The run uses focal loss for label imbalance and validation-tuned coordinate
+thresholds for multi-label decisions. It is a strong public-reference result:
+the validation-selected policy reached test macro-F1 0.5330, while the strongest
+public model card found during this iteration reported test macro-F1 0.519.
+
+## Links
 
-The selected policy is coordinate threshold search optimized on validation
-macro-F1.
+- Kaggle model artifact: https://www.kaggle.com/models/kevin250304/goemotions-roberta-large-focal-sota/Transformers/roberta-large-focal-seed42
+- Training kernel: https://www.kaggle.com/code/kevin250304/goemotions-roberta-large-focal-sweep
+- Dataset: https://huggingface.co/datasets/google-research-datasets/go_emotions
+- GoEmotions paper: https://aclanthology.org/2020.acl-main.372/
 
-| Split | Macro F1 | Micro F1 | Samples F1 | Hamming Loss |
+## Results
+
+| Split | Macro-F1 | Micro-F1 | Samples-F1 | Subset accuracy |
 | --- | ---: | ---: | ---: | ---: |
-| Validation | 0.565864 | 0.596637 | 0.605125 | 0.034424 |
-| Test | 0.533020 | 0.576651 | 0.585942 | 0.035820 |
+| Validation | 0.5659 | 0.5966 | 0.6051 | 0.4784 |
+| Test | 0.5330 | 0.5767 | 0.5859 | 0.4695 |
 
-The per-label threshold candidate reached test macro-F1 `0.534994`, but the
-validation-selected policy is the coordinate threshold policy above.
+Additional threshold candidates on test:
 
-## Training
+| Threshold policy | Test macro-F1 |
+| --- | ---: |
+| Fixed 0.5 | 0.5184 |
+| Global threshold | 0.5320 |
+| Validation coordinate search | 0.5330 |
+| Per-label thresholds | 0.5350 |
 
-- Base model: `FacebookAI/roberta-large`
-- Dataset: `google-research-datasets/go_emotions`, `simplified`
-- Loss: focal loss, alpha `0.38`, gamma `2.8`
-- Epochs: `4`
-- Learning rate: `1e-5`
-- Batch: `2`, gradient accumulation `16`
-- Mixed precision: disabled for stability on Kaggle T4
-- Seed: `42`
+The exported `thresholds.json` stores the validation-selected coordinate
+thresholds used for the headline test metrics.
+
+## Intended Use
+
+Use this model for research, benchmarking, exploratory emotion analysis, and
+building GoEmotions-compatible classifiers. It is best suited to English
+short-form text that resembles the public GoEmotions data distribution.
 
-Training provenance: Kaggle kernel
-`kevin250304/goemotions-roberta-large-focal-sweep`.
+This model should not be used as the sole basis for decisions that affect
+people in high-stakes settings. Emotion labels are subjective, culturally
+dependent, and sensitive to context that may not be present in a single comment.
 
-## Usage
+## Quick Start
 
 ```python
 import json
-import numpy as np
 import torch
+from huggingface_hub import hf_hub_download
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
 repo_id = "AliceYin/goemotions-roberta-large-focal-sota"
+
 tokenizer = AutoTokenizer.from_pretrained(repo_id)
 model = AutoModelForSequenceClassification.from_pretrained(repo_id)
+threshold_data = json.load(open(hf_hub_download(repo_id, "thresholds.json")))
+labels = json.load(open(hf_hub_download(repo_id, "labels.json")))["label_names"]
+threshold_map = threshold_data[threshold_data["selected"]]
+thresholds = [threshold_map[label] for label in labels]
 
-labels = json.load(open("labels.json"))["label_names"]
-thresholds = json.load(open("thresholds.json"))["coordinate"]
-threshold_array = np.array([thresholds[label] for label in labels])
+text = "I finally got this working and I am so relieved."
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=160)
 
-text = "I can't believe this worked so well."
-inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
 with torch.no_grad():
-    probs = torch.sigmoid(model(**inputs).logits).cpu().numpy()[0]
+    probs = torch.sigmoid(model(**inputs).logits)[0]
 
 predicted = [
-    label for label, prob, threshold in zip(labels, probs, threshold_array)
+    {"label": label, "score": float(prob)}
+    for label, prob, threshold in zip(labels, probs, thresholds)
     if prob >= threshold
 ]
 print(predicted)
 ```
 
-For remote loading of thresholds and labels, download `labels.json` and
-`thresholds.json` from this repository alongside the model files.
-
-## Files
+## Training Details
 
-- `model.safetensors`: fine-tuned RoBERTa-large weights
-- `config.json`, `tokenizer.json`, `tokenizer_config.json`: Transformers files
-- `thresholds.json`: fixed, global, per-label, and coordinate thresholds
-- `labels.json`: label names and neutral index
-- `metrics.json`: full validation/test metrics
-- `kaggle-run.log`: training log
+- Base model: `FacebookAI/roberta-large`
+- Dataset: `google-research-datasets/go_emotions`, simplified configuration
+- Loss: focal loss, alpha 0.38, gamma 2.8
+- Epochs: 4
+- Learning rate: 1e-5
+- Batch size: 2 with gradient accumulation 16
+- Mixed precision: disabled for stability
+- Threshold selection: validation macro-F1 coordinate search
+- Seed: 42
+
+## Citation
+
+```bibtex
+@inproceedings{demszky-etal-2020-goemotions,
+  title = "{G}o{E}motions: A Dataset of Fine-Grained Emotions",
+  author = "Demszky, Dorottya and Movshovitz-Attias, Dana and Ko, Jeongwoo and Cowen, Alan and Nemade, Gaurav and Ravi, Sujith",
+  booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
+  year = "2020",
+  pages = "4040--4054"
+}
+```
 
-## Limitations
+## Reproducibility
 
-This model is trained on Reddit comments and inherits the dataset's domain,
-annotation, and class imbalance limitations. Validate before using in
-high-stakes settings.
+The Kaggle artifact includes `metrics.json`, `thresholds.json`, `labels.json`,
+the tokenizer, the model weights, and the Kaggle run log. The training kernel
+and experiment notes record the exact settings used for the reported metrics.