File size: 1,746 Bytes
b0d7d9e ad74172 b885b1d 95bdd1f b0d7d9e ad74172 b0d7d9e ad74172 b0d7d9e ad74172 8441bfc ad74172 b0d7d9e ad74172 b0d7d9e ad74172 b0d7d9e ad74172 b0d7d9e ad74172 b0d7d9e ad74172 b0d7d9e ad74172 b0d7d9e ad74172 b0d7d9e ad74172 b0d7d9e 28d87ad b0d7d9e 28d87ad b0d7d9e 28d87ad b0d7d9e ad74172 28d87ad b0d7d9e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 |
---
library_name: peft
base_model: t5-small
license: apache-2.0
datasets:
- opus100
tags:
- translation
- safetensors
- transformers
language:
- en
- fr
---
# Model Card for Model ID
A language translation model fine-tuned on **opus100** dataset for *English to French* translation.
## Model Description
- **Model type:** Language Model
- **Language(s) (NLP):** English, French
- **License:** Apache 2.0
- **Finetuned from model:** [T5-small](https://huggingface.co/t5-small)
## Uses
The model is intended to use for English to French translation related tasks.
## How to Get Started with the Model
Install necessary libraries
```
pip install transformers peft accelerate
```
Use the code below to get started with the model.
```python
from peft import PeftModel, PeftConfig
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("dmedhi/eng2french-t5-small")
model = AutoModelForSeq2SeqLM.from_pretrained("t5-small")
model = PeftModel.from_pretrained(model, "dmedhi/eng2french-t5-small")
context = tokenizer(["Do you want coffee?"], return_tensors='pt')
output = model.generate(**context)
result = tokenizer.decode(output[0], skip_special_tokens=True)
print(result)
# Output
# Tu veux du café?
```
## Training Details
### Training Data
- Dataset used: [Opus100](https://huggingface.co/datasets/opus100)
- Subset: "en-fr"
## Evaluation
- global_step=5000
- training_loss=1.295289501953125
#### Metrics
- train_runtime = 1672.4371
- train_samples_per_second = 23.917
- train_steps_per_second = 2.99
- total_flos = 685071170273280.0
- train_loss = 1.295289501953125
- epoch = 20.0
## Compute Instance
- Google Colab - T4 GPU (Free)
### Framework versions
- PEFT 0.7.1 |