Deploy TinyModel1 from GitHub Actions

README.md

@@ -1,38 +1,121 @@
 ---
 license: apache-2.0
 library_name: transformers
+pipeline_tag: text-classification
 datasets:
   - ag_news
+language:
+  - en
 tags:
   - tiny
+  - bert
   - text-classification
   - ag-news
 ---
 
+<div align="center">
+  <img src="TinyModel1Image.png" alt="TinyModel1" width="560" />
+</div>
+
 # TinyModel1
 
-TinyModel1 is a lightweight news-topic classifier trained from scratch on `ag_news`.
+**TinyModel1** is a compact **encoder** model for **news topic classification**, trained from scratch on the [AG News](https://huggingface.co/datasets/fancyzhx/ag_news) dataset. It targets fast CPU/GPU inference, simple deployment behind a router or API, and use as a **baseline** before larger or domain-specific models.
+
+---
+
+## Model summary
+
+| | |
+|:--|:--|
+| **Task** | Text classification (single-label, 4 classes) |
+| **Labels** | World, Sports, Business, Sci/Tech |
+| **Architecture** | Tiny BERT-style encoder (`BertForSequenceClassification`) |
+| **Parameters** | 1,339,268 (~1.34M) |
+| **Max sequence length** | 128 tokens (training & inference) |
+| **Framework** | [Transformers](https://github.com/huggingface/transformers) · Safetensors |
+
+---
+
+## Model overview
+
+This release fits a **small footprint** so you can run batch or interactive classification without heavy GPUs. Training uses a WordPiece tokenizer fit on the training split and a shallow BERT stack suited to short news sentences.
 
-## Task
+### **Core capabilities**
 
-Input: short news text  
-Output labels: World, Sports, Business, Sci/Tech
+- **Topic routing** — assign one of four coarse news categories for search, feeds, or moderation triage.
+- **Low latency** — small parameter count keeps inference suitable for edge and serverless setups.
+- **Fine-tuning base** — swap labels or add data for your domain while keeping the same architecture.
 
-## Training setup
+---
+
+## Training
+
+| Setting | Value |
+|:--|:--|
+| **Train samples** | 3000 |
+| **Eval samples** | 600 |
+| **Epochs** | 2 |
+| **Batch size** | 16 |
+| **Learning rate** | 0.0001 |
+| **Optimizer** | AdamW |
+
+---
 
-- Base model: tiny BERT from scratch
-- Train samples: 3000
-- Eval samples: 600
-- Epochs: 2
-- Batch size: 16
-- Learning rate: 0.0001
+## Evaluation
+
+| Metric | Value |
+|:--|:--|
+| **Eval accuracy** | 0.5333 |
+| **Final train loss** | 1.1592 |
+
+Metrics are computed on the held-out eval split configured above; treat them as a **sanity-check baseline**, not a production SLA.
+
+---
 
-## Quick metrics
+## Getting started
 
-- Eval accuracy: 0.5333
-- Final train loss: 1.1577
+### Inference with `transformers`
+
+```python
+from transformers import pipeline
+
+clf = pipeline(
+    "text-classification",
+    model="TinyModel1",  # or local path after save
+    tokenizer="TinyModel1",
+    top_k=None,
+)
+text = "Markets rose after the central bank held rates steady."
+print(clf(text))
+```
+
+Use `top_k=None` (or your Transformers version’s equivalent) to obtain scores for **all** labels. Replace `"TinyModel1"` with your Hugging Face model id (for example `HyperlinksSpace/TinyModel1`) when loading from the Hub.
+
+---
+
+## Training data
+
+- **Dataset:** [fancyzhx/ag_news](https://huggingface.co/datasets/fancyzhx/ag_news) (4-class news topics).
+- **Preprocessing:** tokenizer trained on training texts; sequences truncated to 128 tokens.
+
+---
 
 ## Intended use
 
-- Fast baseline for category routing/classification
-- Starter model for domain adaptation and production experiments
+- Prototyping **routing**, **tagging**, and **dashboard** features over English news-style text.
+- Teaching and benchmarking small-classification setups.
+- Starting point for **domain adaptation** (finance, sports, etc.) with your own labels.
+
+---
+
+## Limitations
+
+- **Accuracy** is modest by design; do not rely on it for high-stakes decisions without validation on your data.
+- **English-oriented** news wording; other languages or social-style text may degrade.
+- **Four fixed classes**; not suitable as a general-purpose language model.
+
+---
+
+## License
+
+This model is released under the **Apache 2.0** license (see repository `LICENSE` where applicable).

artifact.json

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     "Sci/Tech"
   ],
   "eval_accuracy": 0.5333,
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+  "train_loss": 1.1592,
+  "num_parameters": 1339268,
+  "max_train_samples": 3000,
+  "max_eval_samples": 600,
+  "epochs": 2,
+  "batch_size": 16,
+  "learning_rate": 0.0001
 }

model.safetensors

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@@ -6410,8 +6410,8 @@
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@@ -6985,8 +6985,8 @@
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@@ -7074,8 +7074,8 @@
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@@ -7107,8 +7107,8 @@
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@@ -7139,8 +7139,8 @@
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@@ -7159,59 +7159,59 @@
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@@ -7453,9 +7453,9 @@
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@@ -7647,10 +7647,10 @@
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@@ -7730,8 +7730,8 @@
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@@ -7748,8 +7748,8 @@
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@@ -7963,8 +7963,8 @@
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@@ -8045,8 +8045,8 @@
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