Upload folder using huggingface_hub

README.md

@@ -0,0 +1,164 @@
+---
+library_name: transformers.js
+pipeline_tag: token-classification
+base_model: microsoft/xtremedistil-l6-h256-uncased
+language:
+  - en
+license: cc-by-4.0
+tags:
+  - token-classification
+  - slot-filling
+  - ner
+  - transformers.js
+  - onnx
+  - quantized
+datasets:
+  - AmazonScience/massive
+metrics:
+  - f1
+  - precision
+  - recall
+  - accuracy
+model-index:
+  - name: notes-slots
+    results:
+      - task:
+          type: token-classification
+          name: Slot Extraction
+        dataset:
+          name: MASSIVE en-US (+ synthetic productivity/realistic)
+          type: AmazonScience/massive
+        metrics:
+          - type: f1
+            value: 0.8535
+            name: Token F1 (fp32)
+          - type: f1
+            value: 0.7392
+            name: Token F1 (q8, shipped)
+          - type: accuracy
+            value: 0.9550
+            name: Accuracy (fp32)
+---
+
+# notes-slots
+
+Compact token-classification model that extracts scheduling/task slots from short
+English notes — **participants**, **datetimes**, **priorities**, and
+**recurrences** — and runs fully client-side via [Transformers.js](https://huggingface.co/docs/transformers.js).
+
+The shipped artifact is an **INT8-quantized ONNX** bundle (~13 MB) intended for
+in-browser WASM inference, not a PyTorch checkpoint.
+
+## Model details
+
+| | |
+|---|---|
+| Base model | [`microsoft/xtremedistil-l6-h256-uncased`](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) (MIT) |
+| Architecture | `BertForTokenClassification` — 6 layers, hidden size 256, 8 heads, intermediate 1024, vocab 30522, max positions 512 (~13M params) |
+| Task | Token classification (BIO slot tagging) |
+| Schema version | `slot-labels-v0.3.0` |
+| Model version | 0.1.0 |
+| Languages | English |
+| Runtime | Transformers.js v4, WASM device, dtype `q8` |
+| Bundle size | 13.32 MB |
+| `transformers` (training) | 4.57.6 |
+| License | CC BY 4.0 |
+
+### Labels (9, BIO)
+
+`O`, `B-PARTICIPANT`, `I-PARTICIPANT`, `B-PRIORITY`, `I-PRIORITY`,
+`B-DATETIME`, `I-DATETIME`, `B-RECURRENCE`, `I-RECURRENCE`
+
+A bundled `transitions.json` carries empirical BIO transition log-probabilities
+(Laplace-smoothed, invalid transitions hard-zeroed) for optional Viterbi-style
+decoding on top of the raw token logits.
+
+## Intended use
+
+- **In scope:** extracting participants / datetimes / priorities / recurrence
+  cues from short, informal English notes and reminders (calendar, to-do, email
+  intent style text).
+- **Out of scope:** long documents, languages other than English, normalization
+  of extracted spans into structured datetimes (use a downstream parser such as
+  `chrono-node` for that), and any high-stakes decisioning.
+
+## Usage (Transformers.js)
+
+```js
+import { pipeline } from "@huggingface/transformers";
+
+const tagger = await pipeline("token-classification", "jottypro/notes-slots", {
+  dtype: "q8",
+});
+
+const out = await tagger("call Sarah next Friday at 5pm, high priority, every week");
+console.log(out);
+```
+
+The ONNX weights live at `onnx/model_quantized.onnx`, which is the layout
+Transformers.js expects when loading from the Hub.
+
+## Training
+
+- **Data:** [AmazonScience/MASSIVE](https://huggingface.co/datasets/AmazonScience/massive)
+  `en-US` (config `en-US`, revision `d2362678…`), filtered to the
+  `calendar` / `datetime` / `email` / `lists` scenarios with MASSIVE slots
+  remapped onto the local 4-slot schema (e.g. `person`/`relation`/`email_address`
+  → `PARTICIPANT`, `date`/`time`/`time_zone` → `DATETIME`,
+  `general_frequency` → `RECURRENCE`), combined with synthetic
+  *productivity* and *realistic* note generators.
+- **Augmentation:** light, training-split only (`AUGMENT_FACTOR=2`) — random
+  filler-word prefix, trailing punctuation, occasional `O`-token dropout;
+  deduplicated against originals.
+- **Hyperparameters:** 10 epochs with early stopping (patience 2, restore best
+  by F1), batch size 64, learning rate 5e-5, cosine schedule, warmup ratio 0.1,
+  weight decay 0.01, label smoothing 0.1, max sequence length 128, seed 42.
+- **Quantization:** dynamic, per-channel `QInt8`, applied to `MatMul` and
+  `Gather` ops via ONNX Runtime.
+
+## Evaluation
+
+Token-level metrics (seqeval) on the held-out test split (n ≈ 559).
+**The q8 column reflects the artifact actually shipped in this repo.**
+
+| Metric | fp32 | q8 (shipped) |
+|---|---|---|
+| Accuracy | 0.9550 | 0.9050 |
+| Precision | 0.8283 | 0.8718 |
+| Recall | 0.8802 | 0.6416 |
+| **F1** | **0.8535** | **0.7392** |
+| DATETIME F1 | 0.8191 | 0.6724 |
+| PARTICIPANT F1 | 0.9208 | 0.8943 |
+| PRIORITY F1 | 0.7979 | 0.6316 |
+| RECURRENCE F1 | 0.8981 | 0.7093 |
+
+## Limitations and bias
+
+- **Quantization cost:** INT8 quantization raises precision slightly but cuts
+  recall substantially (0.88 → 0.64; F1 0.85 → 0.74). The model misses more
+  true spans than the fp32 model; tune downstream thresholds accordingly.
+- **Domain:** trained on short calendar/task-style English notes plus synthetic
+  data; expect degradation on long-form text, other domains, or other languages.
+- **Synthetic data:** part of the training distribution is generated, so phrasing
+  diversity and demographic coverage of names/relations is limited and may carry
+  generator biases.
+- **No span normalization:** the model tags spans only; converting a `DATETIME`
+  span to an actual timestamp is a downstream concern.
+
+## License and attribution
+
+Released under **CC BY 4.0**, consistent with the MASSIVE training data
+(CC BY 4.0). The base model `microsoft/xtremedistil-l6-h256-uncased` is MIT.
+
+Part of the training data is derived from the MASSIVE dataset; CC BY 4.0
+requires attribution to that source:
+
+```bibtex
+@misc{fitzgerald2022massive,
+  title  = {MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages},
+  author = {FitzGerald, Jack and others},
+  year   = {2022},
+  eprint = {2204.08582},
+  archivePrefix = {arXiv}
+}
+```

manifest.json

@@ -1,8 +1,9 @@
 {
   "base_model": "microsoft/xtremedistil-l6-h256-uncased",
-  "bundle_size_mb": 13.32,
-  "created_at": "2026-05-03T11:46:52.122552+00:00",
+  "bundle_size_mb": 13.325,
+  "created_at": "2026-05-17T10:39:56.428239+00:00",
   "files": {
+    "README.md": "e2d57051221c694b32de2b4152770940ea68dfeae6339581eaa6b6bfa710ae00",
     "config.json": "a42323a8b73212169370a0265018350775bc8556e795c3e3348293ce877f0c26",
     "onnx/model_quantized.onnx": "0b9edd8df5baa28b4a6e4e7b07c796234ab2e19280e38eb7e1705d09462f143f",
     "special_tokens_map.json": "b6d346be366a7d1d48332dbc9fdf3bf8960b5d879522b7799ddba59e76237ee3",