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--- |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:16000 |
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- loss:OnlineContrastiveLoss |
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base_model: jinaai/jina-embeddings-v3 |
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widget: |
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- source_sentence: This is absolutely the worst trash I have ever seen. When I saw |
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it in the theater (arghhh!), it took 15 full minutes before I realized that what |
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I was seeing was the feature, not a sick joke! |
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sentences: |
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- negative negative negative negative |
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- negative negative negative negative |
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- positive positive positive positive |
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- source_sentence: I saw this movie years ago in a group tradition of Fast Forward |
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Film Festivals, where we would set out to rent a bunch of B-movies and vote for |
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who picked the worst.<br /><br />The night we watched this, it was voted the best, |
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due to semblance of plot and fun costuming.<br /><br />This is certainly a silly, |
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kitschy, movie, to be watched under the full understanding that you are watching |
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low-budget fluff. Personally, however, I wouldn't recommend additional substances |
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... this movie will leave it's own mark on you.<br /><br />It made enough of an |
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impression on me that I've actually been trying to get my hands on a copy for |
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a few years.<br /><br />A good choice if you are setting out to watch bad movies. |
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This one is fun, and I remember bouncy music ... |
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sentences: |
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- negative negative negative negative |
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- positive positive positive positive |
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- negative negative negative negative |
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- source_sentence: 'Star Wars: Episode 4 .<br /><br />the best Star Wars ever. its |
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the first movie i ever Sean were the bad guys win and its a very good ending. |
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it really had me wait hing for the next star wars because so match stuff comes |
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along in this movie that you just got to find out more in the last one. whit Al |
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lot of movies i always get the feeling that it could be don bedder but not whit |
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this one. and i Will never ever forget the part were wader tels Luke he is his |
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father.way too cool. also love the Bob feat figure a do hes a back ground player. |
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if you never ever Saw a star wars movie you go to she this one.its the best.<br |
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/><br />thanks Lucas' |
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sentences: |
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- negative negative negative negative |
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- positive positive positive positive |
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- positive positive positive positive |
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- source_sentence: Alain Chabat claims this movie as his original idea but the theme |
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of reluctant lovers who finally get it together is as old, if not older, than |
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Shakespeare.<br /><br />Chabat is a "vieux garcon", happily single and not wanting |
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any member of the opposite sex to disturb his life. He has a problem, 5 sisters |
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and a matriarchal mum - the G7 - who decide he should be married. Enter the delightful, |
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charming Charlotte Gainsbourg and what should be a simple plan. Charlotte has |
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to pose as Chabat's girlfriend and then simply not turn up on the day of the wedding. |
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No more talk of marriage from the G7. Of course the best laid plans have a habit |
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of spiralling out of control.<br /><br />There are very strong supporting roles |
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from Lafont as the mother and Osterman as the tight-fisted brother of Gainsbourg.<br |
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/><br />There are some fantastic scenes as first Charlotte has to charm, then |
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revolt the family. French farce with an English. |
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sentences: |
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- positive positive positive positive |
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- negative negative negative negative |
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- negative negative negative negative |
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- source_sentence: Saw this on cable back in the early 90's and loved it. Never saw |
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it again until it showed up on cable again recently. Still find it a great Vietnam |
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movie. Not sure why its not higher rated. I found everything about this film compelling. |
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As a vet (not from Vietnam) I can relate to the situations brought by both Harris |
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and De Niro. I can only imagine this film being more poignant now with our situation |
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in Iraq. I wish this would be offered on cable more often for people to see. The |
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human toll on our soldiers isn't left on the battlefield. Its brought home for |
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the rest of there lives. And this film is one of many that brings that home in |
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a very hard way. Excellent film. |
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sentences: |
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- negative negative negative negative |
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- positive positive positive positive |
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- positive positive positive positive |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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--- |
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|
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# SentenceTransformer based on jinaai/jina-embeddings-v3 |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [jinaai/jina-embeddings-v3](https://huggingface.co/jinaai/jina-embeddings-v3). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [jinaai/jina-embeddings-v3](https://huggingface.co/jinaai/jina-embeddings-v3) <!-- at revision 62a81741b58448ed8f691764cec7aa5d3c045e4c --> |
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- **Maximum Sequence Length:** 8194 tokens |
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- **Output Dimensionality:** 1024 tokens |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(transformer): Transformer( |
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(auto_model): XLMRobertaLoRA( |
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(roberta): XLMRobertaModel( |
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(embeddings): XLMRobertaEmbeddings( |
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(word_embeddings): ParametrizedEmbedding( |
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250002, 1024, padding_idx=1 |
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(parametrizations): ModuleDict( |
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(weight): ParametrizationList( |
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(0): LoRAParametrization() |
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) |
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) |
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) |
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(token_type_embeddings): ParametrizedEmbedding( |
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1, 1024 |
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(parametrizations): ModuleDict( |
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(weight): ParametrizationList( |
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(0): LoRAParametrization() |
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) |
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) |
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) |
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) |
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(emb_drop): Dropout(p=0.1, inplace=False) |
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(emb_ln): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) |
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(encoder): XLMRobertaEncoder( |
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(layers): ModuleList( |
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(0-23): 24 x Block( |
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(mixer): MHA( |
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(rotary_emb): RotaryEmbedding() |
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(Wqkv): ParametrizedLinearResidual( |
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in_features=1024, out_features=3072, bias=True |
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(parametrizations): ModuleDict( |
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(weight): ParametrizationList( |
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(0): LoRAParametrization() |
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) |
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) |
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) |
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(inner_attn): FlashSelfAttention( |
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(drop): Dropout(p=0.1, inplace=False) |
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) |
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(inner_cross_attn): FlashCrossAttention( |
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(drop): Dropout(p=0.1, inplace=False) |
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) |
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(out_proj): ParametrizedLinear( |
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in_features=1024, out_features=1024, bias=True |
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(parametrizations): ModuleDict( |
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(weight): ParametrizationList( |
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(0): LoRAParametrization() |
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) |
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) |
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) |
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) |
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(dropout1): Dropout(p=0.1, inplace=False) |
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(drop_path1): StochasticDepth(p=0.0, mode=row) |
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(norm1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) |
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(mlp): Mlp( |
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(fc1): ParametrizedLinear( |
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in_features=1024, out_features=4096, bias=True |
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(parametrizations): ModuleDict( |
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(weight): ParametrizationList( |
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(0): LoRAParametrization() |
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) |
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) |
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) |
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(fc2): ParametrizedLinear( |
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in_features=4096, out_features=1024, bias=True |
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(parametrizations): ModuleDict( |
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(weight): ParametrizationList( |
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(0): LoRAParametrization() |
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) |
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) |
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) |
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) |
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(dropout2): Dropout(p=0.1, inplace=False) |
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(drop_path2): StochasticDepth(p=0.0, mode=row) |
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(norm2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) |
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) |
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) |
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) |
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(pooler): XLMRobertaPooler( |
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(dense): ParametrizedLinear( |
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in_features=1024, out_features=1024, bias=True |
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(parametrizations): ModuleDict( |
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(weight): ParametrizationList( |
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(0): LoRAParametrization() |
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) |
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) |
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) |
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(activation): Tanh() |
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) |
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) |
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) |
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) |
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(pooler): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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(normalizer): Normalize() |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("ELVISIO/jina_embeddings_v3_finetuned_online_contrastive_imdb_2") |
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# Run inference |
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sentences = [ |
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"Saw this on cable back in the early 90's and loved it. Never saw it again until it showed up on cable again recently. Still find it a great Vietnam movie. Not sure why its not higher rated. I found everything about this film compelling. As a vet (not from Vietnam) I can relate to the situations brought by both Harris and De Niro. I can only imagine this film being more poignant now with our situation in Iraq. I wish this would be offered on cable more often for people to see. The human toll on our soldiers isn't left on the battlefield. Its brought home for the rest of there lives. And this film is one of many that brings that home in a very hard way. Excellent film.", |
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'positive positive positive positive', |
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'negative negative negative negative', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 1024] |
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|
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 16,000 training samples |
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence1 | sentence2 | label | |
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|:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|:--------------------------------------------------------------| |
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| type | string | string | float | |
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| details | <ul><li>min: 39 tokens</li><li>mean: 173.59 tokens</li><li>max: 291 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 6.0 tokens</li><li>max: 6 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul> | |
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* Samples: |
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| sentence1 | sentence2 | label | |
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|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------|:-----------------| |
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| <code>There are two kinds of 1950s musicals. First you have the glossy MGM productions with big names and great music. And then you have the minor league with a less famous cast, less famous music and second rate directors. 'The Girl Can't Help It' belongs to the latter category. Neither Tom Ewell or Edmond O'Brien became famous and Jayne Mansfield was famous for her... well, never mind. Seems like every decade has its share of Bo Dereks or Pamela Andersons. The plot itself is thin as a razorblade and one can't help suspect that it is mostly an attempt to sell records for Fats Domino, Little Richard or others of the 1950s rock acts that appear in the movie. If that music appeals to you this is worth watching. If not, don't bother.</code> | <code>negative negative negative negative</code> | <code>1.0</code> | |
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| <code>There are two kinds of 1950s musicals. First you have the glossy MGM productions with big names and great music. And then you have the minor league with a less famous cast, less famous music and second rate directors. 'The Girl Can't Help It' belongs to the latter category. Neither Tom Ewell or Edmond O'Brien became famous and Jayne Mansfield was famous for her... well, never mind. Seems like every decade has its share of Bo Dereks or Pamela Andersons. The plot itself is thin as a razorblade and one can't help suspect that it is mostly an attempt to sell records for Fats Domino, Little Richard or others of the 1950s rock acts that appear in the movie. If that music appeals to you this is worth watching. If not, don't bother.</code> | <code>positive positive positive positive</code> | <code>0.0</code> | |
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| <code>Thankfully as a student I have been able to watch "Diagnosis Murder" for a number of years now. It is basically about a doctor who solves murders with the help of his LAPD son, a young doctor and a pathologist. DM provided 8 seasons of exceptional entertainment. What made it different from the many other cop shows and worth watching many times over was its cast and quality of writing. The main cast gave good performances and Dick Van Dyke's entertainer roots shone through with the use of magic, dance and humor. The best aspects of DM was the fast pace, witty scripts and of course the toe tapping score. Sadly it has been unfairly compared to "Murder, She Wrote". DM is far superior boasting more difficult mysteries to solve and more variety. Now it is gone TV is a worse place. Gone are the days of feelgood, family friendly cop shows. Now there is just depressing 'gritty' ones.</code> | <code>positive positive positive positive</code> | <code>1.0</code> | |
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* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss) |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `per_device_train_batch_size`: 64 |
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- `per_device_eval_batch_size`: 64 |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: no |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 64 |
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- `per_device_eval_batch_size`: 64 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 3.0 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.0 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: False |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `use_liger_kernel`: False |
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- `eval_use_gather_object`: False |
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- `batch_sampler`: batch_sampler |
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- `multi_dataset_batch_sampler`: proportional |
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</details> |
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### Training Logs |
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| Epoch | Step | Training Loss | |
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|:-----:|:----:|:-------------:| |
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| 0.2 | 50 | 2.9875 | |
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| 0.4 | 100 | 0.9284 | |
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| 0.6 | 150 | 0.7744 | |
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| 0.8 | 200 | 0.7551 | |
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| 1.0 | 250 | 0.6899 | |
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| 1.2 | 300 | 0.6892 | |
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| 1.4 | 350 | 0.6208 | |
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| 1.6 | 400 | 0.6831 | |
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| 1.8 | 450 | 0.6417 | |
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| 2.0 | 500 | 0.7181 | |
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| 2.2 | 550 | 0.7638 | |
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| 2.4 | 600 | 0.7152 | |
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| 2.6 | 650 | 0.6103 | |
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| 2.8 | 700 | 0.6801 | |
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| 3.0 | 750 | 0.5981 | |
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### Framework Versions |
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- Python: 3.10.12 |
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- Sentence Transformers: 3.1.1 |
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- Transformers: 4.45.2 |
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- PyTorch: 2.5.1+cu121 |
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- Accelerate: 1.1.1 |
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- Datasets: 2.21.0 |
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- Tokenizers: 0.20.3 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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