.gitattributes

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 *.tar.gz filter=lfs diff=lfs merge=lfs -text
 *.ot filter=lfs diff=lfs merge=lfs -text
 *.onnx filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-model.safetensors filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,81 +0,0 @@
----
-license: mit
-thumbnail: https://huggingface.co/front/thumbnails/facebook.png
-pipeline_tag: zero-shot-classification
-datasets:
-- multi_nli
----
-
-# bart-large-mnli
-
-This is the checkpoint for [bart-large](https://huggingface.co/facebook/bart-large) after being trained on the [MultiNLI (MNLI)](https://huggingface.co/datasets/multi_nli) dataset.
-
-Additional information about this model:
-- The [bart-large](https://huggingface.co/facebook/bart-large) model page
-- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
-](https://arxiv.org/abs/1910.13461)
-- [BART fairseq implementation](https://github.com/pytorch/fairseq/tree/master/fairseq/models/bart)
-
-## NLI-based Zero Shot Text Classification
-
-[Yin et al.](https://arxiv.org/abs/1909.00161) proposed a method for using pre-trained NLI models as a ready-made zero-shot sequence classifiers. The method works by posing the sequence to be classified as the NLI premise and to construct a hypothesis from each candidate label. For example, if we want to evaluate whether a sequence belongs to the class "politics", we could construct a hypothesis of `This text is about politics.`. The probabilities for entailment and contradiction are then converted to label probabilities.
-
-This method is surprisingly effective in many cases, particularly when used with larger pre-trained models like BART and Roberta. See [this blog post](https://joeddav.github.io/blog/2020/05/29/ZSL.html) for a more expansive introduction to this and other zero shot methods, and see the code snippets below for examples of using this model for zero-shot classification both with Hugging Face's built-in pipeline and with native Transformers/PyTorch code.
-
-#### With the zero-shot classification pipeline
-
-The model can be loaded with the `zero-shot-classification` pipeline like so:
-
-```python
-from transformers import pipeline
-classifier = pipeline("zero-shot-classification",
-                      model="facebook/bart-large-mnli")
-```
-
-You can then use this pipeline to classify sequences into any of the class names you specify.
-
-```python
-sequence_to_classify = "one day I will see the world"
-candidate_labels = ['travel', 'cooking', 'dancing']
-classifier(sequence_to_classify, candidate_labels)
-#{'labels': ['travel', 'dancing', 'cooking'],
-# 'scores': [0.9938651323318481, 0.0032737774308770895, 0.002861034357920289],
-# 'sequence': 'one day I will see the world'}
-```
-
-If more than one candidate label can be correct, pass `multi_class=True` to calculate each class independently:
-
-```python
-candidate_labels = ['travel', 'cooking', 'dancing', 'exploration']
-classifier(sequence_to_classify, candidate_labels, multi_class=True)
-#{'labels': ['travel', 'exploration', 'dancing', 'cooking'],
-# 'scores': [0.9945111274719238,
-#  0.9383890628814697,
-#  0.0057061901316046715,
-#  0.0018193122232332826],
-# 'sequence': 'one day I will see the world'}
-```
-
-
-#### With manual PyTorch
-
-```python
-# pose sequence as a NLI premise and label as a hypothesis
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-nli_model = AutoModelForSequenceClassification.from_pretrained('facebook/bart-large-mnli')
-tokenizer = AutoTokenizer.from_pretrained('facebook/bart-large-mnli')
-
-premise = sequence
-hypothesis = f'This example is {label}.'
-
-# run through model pre-trained on MNLI
-x = tokenizer.encode(premise, hypothesis, return_tensors='pt',
-                     truncation_strategy='only_first')
-logits = nli_model(x.to(device))[0]
-
-# we throw away "neutral" (dim 1) and take the probability of
-# "entailment" (2) as the probability of the label being true 
-entail_contradiction_logits = logits[:,[0,2]]
-probs = entail_contradiction_logits.softmax(dim=1)
-prob_label_is_true = probs[:,1]
-```

config.json

@@ -1,49 +1,49 @@
 {
-  "_num_labels": 3,
   "activation_dropout": 0.0,
-  "activation_function": "gelu",
-  "add_final_layer_norm": false,
-  "architectures": [
-    "BartForSequenceClassification"
-  ],
+  "architectures": null,
   "attention_dropout": 0.0,
-  "bos_token_id": 0,
   "classif_dropout": 0.0,
-  "classifier_dropout": 0.0,
   "d_model": 1024,
   "decoder_attention_heads": 16,
   "decoder_ffn_dim": 4096,
   "decoder_layerdrop": 0.0,
   "decoder_layers": 12,
-  "decoder_start_token_id": 2,
+  "do_sample": false,
   "dropout": 0.1,
   "encoder_attention_heads": 16,
   "encoder_ffn_dim": 4096,
   "encoder_layerdrop": 0.0,
   "encoder_layers": 12,
-  "eos_token_id": 2,
-  "forced_eos_token_id": 2,
-  "gradient_checkpointing": false,
+  "finetuning_task": null,
   "id2label": {
     "0": "contradiction",
     "1": "neutral",
     "2": "entailment"
   },
   "init_std": 0.02,
-  "is_encoder_decoder": true,
+  "is_decoder": false,
   "label2id": {
     "contradiction": 0,
     "entailment": 2,
     "neutral": 1
   },
+  "length_penalty": 1.0,
+  "max_length": 20,
   "max_position_embeddings": 1024,
   "model_type": "bart",
-  "normalize_before": false,
+  "num_beams": 1,
   "num_hidden_layers": 12,
+  "num_labels": 3,
+  "num_return_sequences": 1,
+  "output_attentions": false,
+  "output_hidden_states": false,
   "output_past": false,
-  "pad_token_id": 1,
-  "scale_embedding": false,
-  "transformers_version": "4.7.0.dev0",
-  "use_cache": true,
+  "pruned_heads": {},
+  "repetition_penalty": 1.0,
+  "temperature": 1.0,
+  "top_k": 50,
+  "top_p": 1.0,
+  "torchscript": false,
+  "use_bfloat16": false,
   "vocab_size": 50265
 }

tokenizer_config.json

@@ -1 +0,0 @@
-{"model_max_length": 1024}