File size: 1,406 Bytes
031500e 9838836 031500e 9838836 031500e 9838836 031500e 9838836 031500e 9838836 031500e 9838836 031500e 9838836 031500e 9838836 031500e 9838836 |
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 |
---
library_name: transformers
datasets:
- cardiffnlp/tweet_eval
base_model:
- OuteAI/Lite-Oute-1-300M-Instruct
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
OuteAI/Lite-Oute-1-300M-Instruct finetuned on cardiffnlp/tweet_eval for sentiment-analysis task with custom LoRA.
## How to Get Started with the Model
Use the code below to get started with the model.
```python
model = AutoModelForCausalLM.from_pretrained(f"efromomr/llm-course-hw3-lora", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(f"efromomr/llm-course-hw3-lora")
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "left"
input_ids = tokenizer(text, return_tensors="pt").input_ids
output_ids = model.generate(input_ids, max_new_tokens=16)
generated_text = tokenizer.decode(output_ids[0][len(input_ids[0]) :], skip_special_tokens=True)
print(generated_text)
#positive
```
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
cardiffnlp/tweet_eval
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Metrics
F1: 0.49 on test set |