Update README.md

README.md

@@ -34,40 +34,87 @@ parameters:
   use_fast: False
 ---
 
+---
+license: other
+tags:
+- generated_from_trainer
+- opt
+- custom-license
+- no-commercial
+- email
+- auto-complete
+- 125m
+datasets:
+- aeslc
+
+widget:
+- text: "Hey <NAME>,\n\nThank you for signing up for my weekly newsletter. Before we get started, you'll have to confirm your email address." 
+  example_title: "newsletter"
+- text: "Hi <NAME>,\n\nI hope this email finds you well. Let me start by saying that I am a big fan of your work." 
+  example_title: "fan"
+- text: "Greetings <NAME>,\n\nI hope you had a splendid evening at the Company sausage eating festival. I am reaching out because" 
+  example_title: "festival"
+- text: "Good Morning <NAME>,\n\nI was just thinking to myself about how much I love creating value" 
+  example_title: "value"
+- text: "URGENT - I need"
+  example_title: "URGENT"
+parameters:
+  min_length: 4
+  max_length: 64
+  length_penalty: 0.7
+  no_repeat_ngram_size: 3
+  do_sample: False
+  num_beams: 4
+  early_stopping: True
+  repetition_penalty: 3.5
+  use_fast: False
+---
+> NOTE: there is currently a bug with huggingface API for OPT models. Please use the [colab notebook](https://colab.research.google.com/gist/pszemraj/033dc9a38da31ced7a0343091ba42e31/email-autocomplete-demo-125m.ipynb) to test :) 
 
-# opt-125m-emailgen-v2_DS-aeslc_Ep-4_Bs-8
+# opt for email generation - 125m
 
-This model is a fine-tuned version of [facebook/opt-125m](https://huggingface.co/facebook/opt-125m) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 2.5552
+Why write the rest of your email when you can generate it?
 
-## Model description
+```
+from transformers import pipeline
+model_tag = "pszemraj/opt-125m-email-generation"
+generator = pipeline(
+              'text-generation', 
+              model=model_tag, 
+              use_fast=False,
+              do_sample=False,
+            )
+            
+prompt = """
+Hello, 
+Following up on the bubblegum shipment."""
+generator(
+    prompt,
+    max_length=96,
+) # generate
+```
+- [colab notebook](https://colab.research.google.com/gist/pszemraj/033dc9a38da31ced7a0343091ba42e31/email-autocomplete-demo-125m.ipynb) for testing/use
 
-More information needed
+## About
 
-## Intended uses & limitations
 
-More information needed
+This model is a fine-tuned version of [facebook/opt-125m](https://huggingface.co/facebook/opt-125m) on an `aeslc` dataset.
 
-## Training and evaluation data
 
-More information needed
+- Emails, phone numbers, etc., were attempted to be excluded in a dataset preparation step using [clean-text](https://pypi.org/project/clean-text/) in Python.
+- Note that API is restricted to generating 64 tokens - you can generate longer emails by using this in a text-generation `pipeline` object
 
-## Training procedure
+It achieves the following results on the evaluation set:
+- Loss: 2.5552
+
+## Intended uses & limitations
+
+- OPT models cannot be used commercially
+
+## Training and evaluation data
 
-### Training hyperparameters
+- the `email_body` field of train + validation (get more data) from the [aeslc](https://huggingface.co/datasets/aeslc) dataset.
 
-The following hyperparameters were used during training:
-- learning_rate: 0.0004
-- train_batch_size: 8
-- eval_batch_size: 8
-- seed: 42
-- distributed_type: multi-GPU
-- gradient_accumulation_steps: 16
-- total_train_batch_size: 128
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: cosine
-- num_epochs: 4
 
 ### Training results