transformers pytorch safetensors distilbert text-classification generated_from_trainer base_model:distilbert-base-uncased doi:10.57967/hf/1042 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: message-contribution
  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. -->

# message-contribution

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on a custom dataset curated by the model engineer.
It achieves the following results on the evaluation set:
- Loss: 0.0015
- Accuracy: 0.9999

## Model description

A binary classifier of text inputs (messages) designed to represent the contribution of messages as `"High"` or `"Low"`.
- **`High`** represents natural language that advances or explicates meaning
- **`Low`** represents cliché, trivial, or non-sensical natural language.

## Intended uses & limitations

Designed for natural language detection and/or weighting of natural language messages.


## Training procedure

```py
# label maps
id2label = {0: "low", 1: "high"}
label2id = {"low": 0, "high": 1}

# auto model
model = AutoModelForSequenceClassification.from_pretrained(
    "distilbert-base-uncased",
    num_labels=2,
    id2label=id2label,
    label2id=label2id,
)
```

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 0.2

### Training results

| Epoch | Step | Val. Loss | Accuracy |
|:-----:|:----:|:---------:|:--------:|
| 0.01  | 10   | 0.4780    | 0.96     |
| 0.02  | 20   | 0.1759    | 0.965    |
| 0.03  | 30   | 0.0477    | 0.995    |
| 0.04  | 40   | 0.1199    | 0.95     |
| 0.05  | 50   | 0.0413    | 0.99     |
| 0.06  | 60   | 0.0068    | 1.0      |
| 0.07  | 70   | 0.0056    | 1.0      |
| 0.08  | 80   | 0.0220    | 0.995    |
| 0.09  | 90   | 0.0081    | 1.0      |
| 0.1   | 100  | 0.0074    | 0.995    |
| 0.11  | 110  | 0.0035    | 1.0      |
| 0.12  | 120  | 0.0030    | 1.0      |
| 0.13  | 130  | 0.0022    | 1.0      |
| 0.14  | 140  | 0.0024    | 1.0      |
| 0.15  | 150  | 0.0021    | 1.0      |
| 0.16  | 160  | 0.0016    | 1.0      |
| 0.17  | 170  | 0.0016    | 1.0      |
| 0.18  | 180  | 0.0016    | 1.0      |
| 0.19  | 190  | 0.0015    | 1.0      |
| 0.2   | 200  | 0.0015    | 1.0      |

### Framework versions

- Transformers 4.32.1
- Pytorch 2.0.1
- Datasets 2.14.4
- Tokenizers 0.13.3
