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snowflake-arctic-xs-grammar-classifier

Text Classification
Transformers
Safetensors
English
bert
Generated from Trainer
grammar-classification
Eval Results
Inference Endpoints
Model card Files Community
snowflake-arctic-xs-grammar-classifier
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---
library_name: transformers
license: mit
base_model: agentlans/snowflake-arctic-embed-xs-zyda-2
tags:
- generated_from_trainer
- text-classification
- grammar-classification
metrics:
- accuracy
model-index:
- name: agentlans/snowflake-arctic-xs-grammar-classifier
  results:
  - task:
      type: text-classification
      name: Grammar Classification
    dataset:
      name: agentlans/grammar-classification
      type: agentlans/grammar-classification
    metrics:
    - type: accuracy
      value: 0.8724
      name: Accuracy
datasets:
- agentlans/grammar-classification
- liweili/c4_200m
language:
- en
pipeline_tag: text-classification
---

# snowflake-arctic-xs-grammar-classifier

This model is a fine-tuned version of [agentlans/snowflake-arctic-embed-xs-zyda-2](https://huggingface.co/agentlans/snowflake-arctic-embed-xs-zyda-2) for grammar classification. It achieves an accuracy of 0.8724 on the evaluation set.

## Model description

The snowflake-arctic-xs-grammar-classifier is designed to classify the grammatical correctness of English sentences. 
It is based on the snowflake-arctic-embed-xs-zyda-2 model and has been fine-tuned on a grammar classification dataset derived from the C4 (Colossal Clean Crawled Corpus).

## Intended uses & limitations

This model is intended for classifying the grammatical correctness of English sentences. It can be used in various applications such as writing assistance tools, educational software, or content moderation systems.

### Usage example

```python
from transformers import pipeline
import torch

device = 0 if torch.cuda.is_available() else -1
classifier = pipeline(
    "text-classification",
    model="agentlans/snowflake-arctic-xs-grammar-classifier",
    device=device,
)

text = "I absolutely loved this movie!"
result = classifier(text)
print(result)  # [{'label': 'grammatical', 'score': 0.8963921666145325}]
```

### Example Classifications

| Status | Text | Explanation |
|:--------:|------|-------------|
| ✔️ | I absolutely loved this movie! | Grammatically correct, clear sentence structure |
| ❌ | How do I shot web? | Grammatically incorrect, improper verb usage |
| ✔️ | Beware the Jabberwock, my son! | Poetic language, grammatically sound |
| ✔️ | Colourless green ideas sleep furiously. | Grammatically correct, though semantically nonsensical |
| ❌ | Has anyone really been far even as decided to use even go want to do look more like? | Completely incoherent and grammatically incorrect |

### Limitations

The model's performance is limited by the quality and diversity of its training data. It may not perform well on specialized or domain-specific text, or on languages other than English. Additionally, it may struggle with complex grammatical structures or nuanced language use.

## Training and evaluation data

The model was trained on the [agentlans/grammar-classification](https://huggingface.co/datasets/agentlans/grammar-classification) dataset, which contains 600 000 examples for binary classification of grammatical correctness in English. This dataset is derived from a subset of the C4_200M Synthetic Dataset for Grammatical Error Correction.

## Training procedure

### Training hyperparameters

- Learning rate: 5e-05
- Batch size: 128
- Number of epochs: 10
- Optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08
- Learning rate scheduler: Linear

<details>
<summary>📊 Detailed Training Results</summary>

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Input Tokens Seen |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-----------------:|
| 0.5192        | 1.0   | 3750  | 0.4722          | 0.7738   | 61&thinsp;440&thinsp;000        |
| 0.4875        | 2.0   | 7500  | 0.4521          | 0.7881   | 122&thinsp;880&thinsp;000       |
| 0.4590        | 3.0   | 11250 | 0.3895          | 0.8227   | 184&thinsp;320&thinsp;000       |
| 0.4351        | 4.0   | 15000 | 0.3981          | 0.8197   | 245&thinsp;760&thinsp;000       |
| 0.4157        | 5.0   | 18750 | 0.3690          | 0.8337   | 307&thinsp;200&thinsp;000       |
| 0.3955        | 6.0   | 22500 | 0.3260          | 0.8585   | 368&thinsp;640&thinsp;000       |
| 0.3788        | 7.0   | 26250 | 0.3267          | 0.8566   | 430&thinsp;080&thinsp;000       |
| 0.3616        | 8.0   | 30000 | 0.3192          | 0.8621   | 491&thinsp;520&thinsp;000       |
| 0.3459        | 9.0   | 33750 | 0.3017          | 0.8707   | 552&thinsp;960&thinsp;000       |
| 0.3382        | 10.0  | 37500 | 0.2971          | 0.8724   | 614&thinsp;400&thinsp;000       |

</details>

### Framework versions

- Transformers: 4.46.3
- PyTorch: 2.5.1+cu124
- Datasets: 3.2.0
- Tokenizers: 20.3