Upload best classification model (flan-t5-small)

README.md

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+---
+license: apache-2.0
+language: en
+tags:
+  - temporal
+  - time-module
+  - pieces
+  - intent-classification
+  - temporal-intent
+datasets:
+  - Pieces/temporal-intent-classification-dataset-split
+base_model: google/flan-t5-small
+metrics:
+  - accuracy
+  - f1
+pipeline_tag: text-classification
+---
+
+# TIME-Module: Classification — flan-t5-small
+
+## Model Description
+
+Temporal intent classification model trained on the split dataset. Classifies user queries into 6 temporal intent categories: Action Scheduling, Content Retrieval, Current Status, Future Information/Planning, Non-Temporal, and Temporal - General.
+
+## Training Details
+
+- **Base Model:** [google/flan-t5-small](https://huggingface.co/google/flan-t5-small)
+- **Architecture:** T5ForSequenceClassification
+- **Dataset:** [Pieces/temporal-intent-classification-dataset-split](https://huggingface.co/datasets/Pieces/temporal-intent-classification-dataset-split) (15,488 train / 1,721 val / 4,303 test)
+- **Training Steps:** 17,500
+- **Learning Rate:** 3e-4
+- **Batch Size:** 64 × 2
+- **Mixed Precision:** bf16
+- **Hardware:** NVIDIA RTX 4090 (24 GB)
+
+## Results
+
+| Metric | Value |
+|--------|-------|
+| accuracy | 95.91% |
+| f1_weighted | ~95.9% |
+
+## Usage
+
+```python
+from transformers import pipeline
+
+pipe = pipeline('text-classification', model='Pieces/time-classification-flan-t5-small-split-best')
+result = pipe('What meetings do I have tomorrow?')
+
+
+
+```
+
+## Part of the TIME-Module Project
+
+This model is part of the TIME (Temporal Intent, Mapping, and Extraction) module, a suite of models for understanding and processing temporal information in natural language.
+
+**Related models:**
+- [Pieces/time-classification-flan-t5-small-split-best](https://huggingface.co/Pieces/time-classification-flan-t5-small-split-best) — Intent classification
+- [Pieces/time-mapping-flan-t5-small-quality-best](https://huggingface.co/Pieces/time-mapping-flan-t5-small-quality-best) — Span prediction (best)
+- [Pieces/time-mapping-t5gemma-270m-best](https://huggingface.co/Pieces/time-mapping-t5gemma-270m-best) — Span prediction (T5Gemma)
+
+## Citation
+
+```bibtex
+@software{time_module,
+  title={TIME-Module: Temporal Intent, Mapping, and Extraction},
+  author={Pieces},
+  year={2026},
+  url={https://huggingface.co/Pieces}
+}
+```

config.json

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+{
+  "architectures": [
+    "T5ForSequenceClassification"
+  ],
+  "classifier_dropout": 0.0,
+  "d_ff": 1024,
+  "d_kv": 64,
+  "d_model": 512,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "gelu_new",
+  "dropout_rate": 0.1,
+  "dtype": "float32",
+  "eos_token_id": 1,
+  "feed_forward_proj": "gated-gelu",
+  "id2label": {
+    "0": "Action/Scheduling",
+    "1": "Content Retrieval",
+    "2": "Current Status",
+    "3": "Future Information/Planning",
+    "4": "Non-Temporal",
+    "5": "Temporal - General"
+  },
+  "initializer_factor": 1.0,
+  "is_decoder": false,
+  "is_encoder_decoder": true,
+  "is_gated_act": true,
+  "label2id": {
+    "Action/Scheduling": 0,
+    "Content Retrieval": 1,
+    "Current Status": 2,
+    "Future Information/Planning": 3,
+    "Non-Temporal": 4,
+    "Temporal - General": 5
+  },
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "n_positions": 512,
+  "num_decoder_layers": 8,
+  "num_heads": 6,
+  "num_layers": 8,
+  "output_past": true,
+  "pad_token_id": 0,
+  "problem_type": "single_label_classification",
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "scale_decoder_outputs": true,
+  "task_specific_params": {
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+      "early_stopping": true,
+      "length_penalty": 2.0,
+      "max_length": 200,
+      "min_length": 30,
+      "no_repeat_ngram_size": 3,
+      "num_beams": 4,
+      "prefix": "summarize: "
+    },
+    "translation_en_to_de": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to German: "
+    },
+    "translation_en_to_fr": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to French: "
+    },
+    "translation_en_to_ro": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to Romanian: "
+    }
+  },
+  "tie_word_embeddings": true,
+  "transformers_version": "5.0.0",
+  "use_cache": true,
+  "vocab_size": 32128
+}

model.safetensors

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+size 243134408

tokenizer_config.json

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