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---
tags:
- generated_from_trainer
datasets:
- postbot/multi-emails-hq
metrics:
- accuracy
model-index:
- name: pythia-160m-hq-emails-v4
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: postbot/multi-emails-hq
type: postbot/multi-emails-hq
metrics:
- name: Accuracy
type: accuracy
value: 0.611281497151223
widget:
- text: >-
Good Morning Professor Beans,
Hope you are doing well. I just wanted to reach out and ask if differential
calculus will be on the exam
example_title: email to prof
- text: >-
Hey <NAME>,
Thank 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>,
I hope this email finds you well. I wanted to reach out and ask about office
hours
example_title: office hours
- text: >-
Greetings <NAME>,
I hope you had a splendid evening at the Company sausage eating festival. I
am reaching out because
example_title: festival
- text: |-
Good Morning Harold,
I was wondering when the next
example_title: event
- text: URGENT - I need the TPS reports
example_title: URGENT
- text: |-
Hi Archibald,
I hope this email finds you extremely well.
example_title: emails that find you
- text: |-
Hello there.
I just wanted to reach out and check in to
example_title: checking in
- text: >-
Hello <NAME>,
I hope this email finds you well. I wanted to reach out and see if you've
enjoyed your time with us
example_title: work well
- text: >-
Hi <NAME>,
I hope this email finds you well. I wanted to reach out and see if we could
catch up
example_title: catch up
- text: >-
I'm <NAME> and I just moved into the area and wanted to reach out and get
some details on where I could get groceries and
example_title: grocery
inference:
parameters:
min_length: 16
max_length: 64
no_repeat_ngram_size: 4
do_sample: true
top_k: 40
top_p: 0.95
repetition_penalty: 3.5
license: apache-2.0
language:
- en
pipeline_tag: text-generation
---
# pythia-160m-hq-emails-v4
This model is a fine-tuned version of [EleutherAI/pythia-160m-deduped](https://huggingface.co/EleutherAI/pythia-160m-deduped) on the postbot/multi-emails-hq dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2856
- Accuracy: 0.6113
- perplexity: 9.8313
## Model description
this is v4
## 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: 0.0006
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 4.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.412 | 0.99 | 76 | 2.5027 | 0.5458 |
| 1.9702 | 1.99 | 152 | 2.2757 | 0.5850 |
| 1.4628 | 2.99 | 228 | 2.2162 | 0.6082 |
| 1.1662 | 3.99 | 304 | 2.2856 | 0.6113 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.1 |