v1.0.0

README.md

@@ -0,0 +1,77 @@
+---
+license: other
+base_model: sentence-transformers/all-MiniLM-L6-v2
+pipeline_tag: text-classification
+language:
+  - en
+tags:
+  - finance
+  - transactions
+  - text-classification
+---
+
+# Finipher transaction classifier v1.0.0
+
+Sorts one bank or card transaction line into one of 17 spending categories. It is a fine-tuned
+`all-MiniLM-L6-v2` (`BertForSequenceClassification`), so it loads with the standard Transformers
+classes and needs no custom code.
+
+Internal checkpoint name: `finance-multitask-minilm-two-head-v2`. Finipher's manifest version:
+`minilm-two-head-20261027-v2`.
+
+## Labels
+
+Education, Entertainment, Fees, Groceries, Healthcare, Income, Insurance, Mortgage, Personal Care,
+Rent, Restaurants, Shopping, Subscription, Transfer, Transportation, Travel, Utilities.
+
+## Input
+
+- One line per transaction, prefixed with its direction: `[debit] STARBUCKS #1234 SEATTLE WA` or
+  `[credit] ACME CORP PAYROLL`.
+- Maximum length 64 tokens.
+- Below 0.50 confidence, don't trust the label.
+
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+
+repo = "finipher/finipher-transaction-classifier"
+tok = AutoTokenizer.from_pretrained(repo, revision="v1.0.0")
+model = AutoModelForSequenceClassification.from_pretrained(repo, revision="v1.0.0").eval()
+
+x = tok(["[debit] NETFLIX.COM 866-579-7172 CA"], truncation=True, max_length=64, return_tensors="pt")
+with torch.inference_mode():
+    p = torch.softmax(model(**x).logits, dim=-1)[0]
+i = int(p.argmax())
+print(model.config.id2label[i], float(p[i]))
+```
+
+## Results
+
+Measured offline through Finipher's production code:
+- 1,290 hand-written everyday vendor lines (Plaid names, card swipes, POS, ACH): 90.7% correct.
+- DoDataThings 68k rows, final Finipher category: 98.7%.
+
+## Known mistakes
+
+- Walmart paychecks (`[credit] WALMART DIR DEP PPD`) read as Shopping.
+- FasTrak and Circle K card lines read as Groceries.
+- Mortgage `PRINCIPAL PMT` reads as Utilities.
+
+## Training data
+
+To be confirmed. This repo stays private until the training data and the rights to it are
+documented.
+
+## Use
+
+The output is a category label, not tax or financial advice.
+
+## Files and SHA-256
+
+| File | SHA-256 |
+|---|---|
+| `config.json` | `6b8c456706017e7cbddd8a4826a88746294100336be5aa9e40f7730890f1b72b` |
+| `model.safetensors` | `96d95c2520f9b8cee0a3e85e1440f541420c4c043e18a0c4579943e08e91a5a3` |
+| `tokenizer.json` | `41b9d12a4c559e4098f30173bc0f76d092c43d7b1f873e7027da45a256f30f87` |
+| `tokenizer_config.json` | `1128dd50ff3f913671a9405619c1bd8f9e9ddc28f62257cb94ed70683590b715` |

config.json

@@ -0,0 +1,69 @@
+{
+  "add_cross_attention": false,
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": null,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "eos_token_id": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "id2label": {
+    "0": "Education",
+    "1": "Entertainment",
+    "2": "Fees",
+    "3": "Groceries",
+    "4": "Healthcare",
+    "5": "Income",
+    "6": "Insurance",
+    "7": "Mortgage",
+    "8": "Personal Care",
+    "9": "Rent",
+    "10": "Restaurants",
+    "11": "Shopping",
+    "12": "Subscription",
+    "13": "Transfer",
+    "14": "Transportation",
+    "15": "Travel",
+    "16": "Utilities"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "is_decoder": false,
+  "label2id": {
+    "Education": 0,
+    "Entertainment": 1,
+    "Fees": 2,
+    "Groceries": 3,
+    "Healthcare": 4,
+    "Income": 5,
+    "Insurance": 6,
+    "Mortgage": 7,
+    "Personal Care": 8,
+    "Rent": 9,
+    "Restaurants": 10,
+    "Shopping": 11,
+    "Subscription": 12,
+    "Transfer": 13,
+    "Transportation": 14,
+    "Travel": 15,
+    "Utilities": 16
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "tie_word_embeddings": true,
+  "transformers_version": "5.19.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:96d95c2520f9b8cee0a3e85e1440f541420c4c043e18a0c4579943e08e91a5a3
+size 90891060

tokenizer_config.json

@@ -0,0 +1,24 @@
+{
+  "backend": "tokenizers",
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "is_local": true,
+  "local_files_only": true,
+  "mask_token": "[MASK]",
+  "max_length": 128,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}