Update README.md

README.md

@@ -1,62 +1,35 @@
----
-license: apache-2.0
-base_model: distilbert/distilbert-base-uncased
-tags:
-- generated_from_trainer
-metrics:
-- accuracy
-model-index:
-- name: birthday-detector
-  results: []
----
-
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
-# birthday-detector
-
-This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.1095
-- Accuracy: 0.9796
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 16
-- eval_batch_size: 16
-- seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- num_epochs: 3
-
-### Training results
-
-| Training Loss | Epoch | Step | Validation Loss | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|
-| No log        | 1.0   | 58   | 0.1248          | 0.9592   |
-| No log        | 2.0   | 116  | 0.1100          | 0.9592   |
-| No log        | 3.0   | 174  | 0.1095          | 0.9796   |
-
-
-### Framework versions
-
-- Transformers 4.40.2
-- Pytorch 2.3.0+cpu
-- Datasets 2.19.1
-- Tokenizers 0.19.1
+---
+license: mit
+base_model: distilbert/distilbert-base-uncased
+metrics:
+  - accuracy
+model-index:
+  - name: birthday-detector
+    results: []
+datasets:
+  - nroggendorff/doug
+language:
+  - en
+pipeline_tag: text-classification
+---
+
+# Birthday Detector
+
+This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased).
+It achieves the following results on the evaluation set:
+
+- Loss: 0.0101
+- Accuracy: 1.0
+
+You can easily run it with inference, or run it in python with:
+
+```
+from transformers import pipeline
+classifier = pipeline("sentiment-analysis", model="nroggendorff/birthday-detector")
+
+isBirthday = classifier("happy birthday doug")[0]["label"] == "POSITIVE"
+```
+
+Make sure you have the necessary dependencies installed.
+
+[This models inspiration](https://youtu.be/Q6fjwHPVqjQ)