| --- |
| language: multilingual |
| license: mit |
| base_model: MoritzLaurer/multilingual-MiniLMv2-L6-mnli-xnli |
| tags: |
| - nli |
| - zero-shot-classification |
| - text-classification |
| pipeline_tag: zero-shot-classification |
| --- |
| |
| # SriRamanaAtmic/AtmicNLI |
|
|
| Fine-tuned from [MoritzLaurer/multilingual-MiniLMv2-L6-mnli-xnli](https://huggingface.co/MoritzLaurer/multilingual-MiniLMv2-L6-mnli-xnli) for natural |
| language inference (entailment / neutral / contradiction) on Sri Ramana Maharshi |
| teaching-corpus query/span pairs. |
|
|
| ## Training data |
|
|
| - 723 train examples, 183 held-out validation examples |
| - Validation split is grouped by original query (paraphrases of the same query |
| are never split across train/val) and stratified by label |
|
|
| ## Validation results |
|
|
| ``` |
| precision recall f1-score support |
| |
| entailment 0.6081 0.6522 0.6294 69 |
| neutral 0.7907 0.6296 0.7010 54 |
| contradiction 0.3939 0.4333 0.4127 60 |
| |
| accuracy 0.5738 183 |
| macro avg 0.5976 0.5717 0.5810 183 |
| weighted avg 0.5918 0.5738 0.5795 183 |
| |
| Confusion matrix (rows=gold, cols=predicted): |
| entailment neutral contradiction |
| entailment 45 3 21 |
| neutral 1 34 19 |
| contradiction 28 6 26 |
| ``` |
|
|
| ## Usage |
|
|
| ```python |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer |
| |
| tokenizer = AutoTokenizer.from_pretrained("SriRamanaAtmic/AtmicNLI") |
| model = AutoModelForSequenceClassification.from_pretrained("SriRamanaAtmic/AtmicNLI") |
| |
| premise = "..." # span / context |
| hypothesis = "..." # query / claim |
| inputs = tokenizer(premise, hypothesis, truncation="only_first", return_tensors="pt") |
| logits = model(**inputs).logits |
| ``` |
|
|
