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CESoftmaxAccuracyEvaluator_AllNLI-dev_results.csv

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README.md

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+---
+datasets:
+- multi_nli
+- snli
+language: en
+license: apache-2.0
+metrics:
+- accuracy
+pipeline_tag: zero-shot-classification
+tags:
+- microsoft/deberta-v3-xsmall
+---
+
+# Cross-Encoder for Natural Language Inference
+
+This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. This model is based on [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall)
+
+## Training Data
+The model was trained on the [SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.
+
+## Performance
+- Accuracy on SNLI-test dataset: 91.64
+- Accuracy on  MNLI mismatched set: 87.77
+
+For futher evaluation results, see [SBERT.net - Pretrained Cross-Encoder](https://www.sbert.net/docs/pretrained_cross-encoders.html#nli).
+
+## Usage
+Pre-trained models can be used like this:
+```python
+from sentence_transformers import CrossEncoder
+model = CrossEncoder('cross-encoder/nli-deberta-v3-xsmall')
+scores = model.predict([('A man is eating pizza', 'A man eats something'), ('A black race car starts up in front of a crowd of people.', 'A man is driving down a lonely road.')])
+
+#Convert scores to labels
+label_mapping = ['contradiction', 'entailment', 'neutral']
+labels = [label_mapping[score_max] for score_max in scores.argmax(axis=1)]
+```
+
+## Usage with Transformers AutoModel
+You can use the model also directly with Transformers library (without SentenceTransformers library):
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/nli-deberta-v3-xsmall')
+tokenizer = AutoTokenizer.from_pretrained('cross-encoder/nli-deberta-v3-xsmall')
+
+features = tokenizer(['A man is eating pizza', 'A black race car starts up in front of a crowd of people.'], ['A man eats something', 'A man is driving down a lonely road.'],  padding=True, truncation=True, return_tensors="pt")
+
+model.eval()
+with torch.no_grad():
+    scores = model(**features).logits
+    label_mapping = ['contradiction', 'entailment', 'neutral']
+    labels = [label_mapping[score_max] for score_max in scores.argmax(dim=1)]
+    print(labels)
+```
+
+## Zero-Shot Classification
+This model can also be used for zero-shot-classification:
+```python
+from transformers import pipeline
+
+classifier = pipeline("zero-shot-classification", model='cross-encoder/nli-deberta-v3-xsmall')
+
+sent = "Apple just announced the newest iPhone X"
+candidate_labels = ["technology", "sports", "politics"]
+res = classifier(sent, candidate_labels)
+print(res)
+``` 

added_tokens.json

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config.json

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+{
+  "_name_or_path": "microsoft/deberta-v3-xsmall",
+  "architectures": [
+    "DebertaV2ForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "id2label": {
+    "0": "contradiction",
+    "1": "entailment",
+    "2": "neutral"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "label2id": {
+    "contradiction": 0,
+    "entailment": 1,
+    "neutral": 2
+  },
+  "layer_norm_eps": 1e-7,
+  "max_position_embeddings": 512,
+  "max_relative_positions": -1,
+  "model_type": "deberta-v2",
+  "norm_rel_ebd": "layer_norm",
+  "num_attention_heads": 6,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_dropout": 0,
+  "pooler_hidden_act": "gelu",
+  "pooler_hidden_size": 384,
+  "pos_att_type": [
+    "p2c",
+    "c2p"
+  ],
+  "position_biased_input": false,
+  "position_buckets": 256,
+  "relative_attention": true,
+  "share_att_key": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.11.3",
+  "type_vocab_size": 0,
+  "vocab_size": 128100
+}

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tokenizer_config.json

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