sentiment-polish-gpt2-small

This model was trained from polish-gpt2-small on the polemo2-official dataset. It achieves the following results on the evaluation set:

• Loss: 0.4659
• Accuracy: 0.9627

Model description

Trained from polish-gpt2-small

Intended uses & limitations

Sentiment analysis - neutral/negative/positive/ambiguous

How to use

Transformers AutoModel

from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"
checkpoint = "nie3e/sentiment-polish-gpt2-small"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSequenceClassification.from_pretrained(
    checkpoint
).to(device)

text = "Jak na cenę - super . Śniadanie nie do przejedzenia ."

input_ids = tokenizer(text, return_tensors="pt").to(device)
logits = model(**input_ids)["logits"].to("cpu")

percent = torch.sigmoid(logits).squeeze(dim=0)

id2class = model.config.id2label
print({id2class[i]: f"{(p*100):.2f}%" for i, p in enumerate(percent.tolist())})

{'NEUTRAL': '2.95%', 'NEGATIVE': '0.21%', 'POSITIVE': '100.00%', 'AMBIGUOUS': '20.56%'}

Transformers Pipeline

from transformers import pipeline

pipe = pipeline(
    "sentiment-analysis",
    "nie3e/sentiment-polish-gpt2-small"
)

result = pipe("Jak na cenę - super . Śniadanie nie do przejedzenia .")
print(result)

[{'label': 'POSITIVE', 'score': 0.9999890327453613}]

vLLM >= 0.9.2

from vllm import LLM
llm = LLM(
    "nie3e/sentiment-polish-gpt2-small",
    task="classify",
    enforce_eager=True
)

text = ["Jak na cenę - super . Śniadanie nie do przejedzenia ."]
outputs = llm.classify(text)

for output in outputs:
    print(output.outputs.probs)

vLLM OpenAI serving (recommended)

docker run --gpus 1 --ipc=host -p 8000:8000 vllm/vllm-openai:v0.9.2 --model nie3e/sentiment-polish-gpt2-small

using curl:

curl -X 'POST' \
  'http://127.0.0.1:8000/classify' \
  -H 'Content-Type: application/json' \
  -d '{
  "model": "nie3e/sentiment-polish-gpt2-small",
  "input": ["Przestronny hotel , jasny , z dużymi oknami .", "Położony całkiem blisko centrum ."]
}'

result:

{
	"id": "classify-6619cecdb01a4bf8900df136a9b33b15",
	"object": "list",
	"created": 1749994841,
	"model": "nie3e/sentiment-polish-gpt2-small",
	"data": [
		{
			"index": 0,
			"label": "POSITIVE",
			"probs": [
				0.000006198883056640625,
				1.7881393432617188e-7,
				1.0,
				0.000007569789886474609
			],
			"num_classes": 4
		},
		{
			"index": 1,
			"label": "AMBIGUOUS",
			"probs": [
				0.00013005733489990234,
				0.004421234130859375,
				0.005367279052734375,
				0.990234375
			],
			"num_classes": 4
		}
	],
	"usage": {
		"prompt_tokens": 17,
		"total_tokens": 17,
		"completion_tokens": 0,
		"prompt_tokens_details": null
	}
}

using python:

import requests

response = requests.post(
    f"http://127.0.0.1:8000/classify",
    headers={"Content-Type": "application/json"},
    json={
        "model": "nie3e/sentiment-polish-gpt2-small",
        "input": [
            "Przestronny hotel , jasny , z dużymi oknami .",
            "Położony całkiem blisko centrum ."
        ]
    }
)

print(response.json())

{'id': 'classify-ac86189c5d0e41908584c3e88d356316',
 'object': 'list',
 'created': 1753209126,
 'model': 'nie3e/sentiment-polish-gpt2-small',
 'data': [{'index': 0,
   'label': 'POSITIVE',
   'probs': [6.277556167333387e-06,
    1.9292743047572003e-07,
    0.9999858140945435,
    7.631567314092536e-06],
   'num_classes': 4},
  {'index': 1,
   'label': 'AMBIGUOUS',
   'probs': [0.0001290593936573714,
    0.004407374653965235,
    0.005339722614735365,
    0.9901238083839417],
   'num_classes': 4}],
 'usage': {'prompt_tokens': 17,
  'total_tokens': 17,
  'completion_tokens': 0,
  'prompt_tokens_details': None}}

Training and evaluation data

Merged all rows from polemo2-official dataset.

Train/test split: 80%/20%

Datacollator:

from transformers import DataCollatorWithPadding
data_collator = DataCollatorWithPadding(
  tokenizer=tokenizer,
  padding="longest",
  max_length=128,
  pad_to_multiple_of=8
)

Training procedure

GPU: RTX 3090

Training time: 2:53:05

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10
• mixed_precision_training: Native AMP

Training results

Evaluation

Evaluated on allegro/klej-polemo2-out test dataset.

from datasets import load_dataset
from evaluate import evaluator

data = load_dataset("allegro/klej-polemo2-out", split="test").shuffle(seed=42)
task_evaluator = evaluator("text-classification")

# fix labels
l = {
        "__label__meta_zero": 0,
        "__label__meta_minus_m": 1,
        "__label__meta_plus_m": 2,
        "__label__meta_amb": 3
    }
def fix_labels(examples):
    examples["target"] = l[examples["target"]]
    return examples
data = data.map(fix_labels)

eval_resutls = task_evaluator.compute(
    model_or_pipeline="nie3e/sentiment-polish-gpt2-small",
    data=data,
    label_mapping={"NEUTRAL": 0, "NEGATIVE": 1, "POSITIVE": 2, "AMBIGUOUS": 3},
    input_column="sentence",
    label_column="target"
)

print(eval_resutls)

{
    "accuracy": 0.9838056680161943,
    "total_time_in_seconds": 5.2441766999982065,
    "samples_per_second": 94.1997244296076,
    "latency_in_seconds": 0.010615742307688678
}

Framework versions

• Transformers 4.36.2
• Pytorch 2.1.2+cu121
• Datasets 2.16.1
• Tokenizers 0.15.0