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Upload RobertaForSequenceClassification

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  1. README.md +4 -12
  2. config.json +3 -3
  3. model.safetensors +2 -2
README.md CHANGED
@@ -4,19 +4,12 @@ language:
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  license: mit
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  datasets:
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  - cardiffnlp/super_tweeteval
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- pipeline_tag: text-classification
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- inference:
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- parameters:
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- function_to_apply: none
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- widget:
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- - text: >-
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- Looooooool what is this story #TalksWithAsh </s> For someone who keeps
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- saying long story short, the story is quite long iyah #TalksWithAsh
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  ---
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  # cardiffnlp/twitter-roberta-large-similarity-latest
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  This is a RoBERTa-large model trained on 154M tweets until the end of December 2022 and finetuned for tweet similarity (regression on two texts) on the _TweetSIM_ dataset of [SuperTweetEval](https://huggingface.co/datasets/cardiffnlp/super_tweeteval).
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- The original Twitter-larged RoBERTa model can be found [here](https://huggingface.co/cardiffnlp/twitter-roberta-large-2022-154m).
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  ## Example
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  ```python
@@ -32,9 +25,8 @@ text_2 = 'For someone who keeps saying long story short, the story is quite long
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  text_input = f"{text_1} </s> {text_2}"
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- pipe = pipeline('text-classification', model=model, tokenizer=tokenizer, function_to_apply="none")
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- pipe(text_input)
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- >> [{'label': 'LABEL_0', 'score': 2.956475019454956}]
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  ```
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  license: mit
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  datasets:
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  - cardiffnlp/super_tweeteval
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+ pipeline_tag: sentence-similarity
 
 
 
 
 
 
 
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  ---
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  # cardiffnlp/twitter-roberta-large-similarity-latest
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  This is a RoBERTa-large model trained on 154M tweets until the end of December 2022 and finetuned for tweet similarity (regression on two texts) on the _TweetSIM_ dataset of [SuperTweetEval](https://huggingface.co/datasets/cardiffnlp/super_tweeteval).
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+ The original Twitter-based RoBERTa model can be found [here](https://huggingface.co/cardiffnlp/twitter-roberta-large-2022-154m).
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  ## Example
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  ```python
 
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  text_input = f"{text_1} </s> {text_2}"
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+ model(**tokenizer(text_input, return_tensors="pt")).logits
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+ >>tensor([[2.9565]])
 
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  ```
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config.json CHANGED
@@ -1,7 +1,7 @@
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  {
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- "_name_or_path": "../../best_models/troberta-large-tweet-similarity/best_model/",
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  "architectures": [
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- "RobertaModel"
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  ],
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  "attention_probs_dropout_prob": 0.1,
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  "bos_token_id": 0,
@@ -27,7 +27,7 @@
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  "position_embedding_type": "absolute",
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  "problem_type": "regression",
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  "torch_dtype": "float32",
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- "transformers_version": "4.35.0",
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  "type_vocab_size": 1,
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  "use_cache": true,
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  "vocab_size": 50265
 
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  {
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+ "_name_or_path": "best_models/troberta-large-tweet-similarity/best_model",
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  "architectures": [
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+ "RobertaForSequenceClassification"
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  ],
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  "attention_probs_dropout_prob": 0.1,
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  "bos_token_id": 0,
 
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  "position_embedding_type": "absolute",
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  "problem_type": "regression",
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  "torch_dtype": "float32",
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+ "transformers_version": "4.38.2",
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  "type_vocab_size": 1,
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  "use_cache": true,
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  "vocab_size": 50265
model.safetensors CHANGED
@@ -1,3 +1,3 @@
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