Add SetFit model
Browse files- 1_Pooling/config.json +10 -0
- README.md +311 -0
- config.json +24 -0
- config_sentence_transformers.json +10 -0
- config_setfit.json +7 -0
- model.safetensors +3 -0
- model_head.pkl +3 -0
- modules.json +14 -0
- sentence_bert_config.json +4 -0
- special_tokens_map.json +51 -0
- tokenizer.json +0 -0
- tokenizer_config.json +66 -0
- vocab.txt +0 -0
1_Pooling/config.json
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{
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"word_embedding_dimension": 768,
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false,
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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": false,
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"include_prompt": true
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}
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README.md
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---
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base_model: sentence-transformers/paraphrase-mpnet-base-v2
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datasets:
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- stanfordnlp/imdb
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library_name: setfit
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metrics:
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- accuracy
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pipeline_tag: text-classification
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tags:
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- setfit
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- sentence-transformers
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- text-classification
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- generated_from_setfit_trainer
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widget:
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- text: What does the " Executive producer " do in a movie . If I remember correctly
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it's the person who raised the financial backing to make the movie . You might
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notice in a great number of movies starring Sean Connery that he is also the executive
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producer which meant Connery himself raised the money since he is a major player
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. Unfortunately it should also be pointed out that a great number of movies "
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starring Sean Connery were solely made because he managed to raise the money since
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he's a major Hollywood player , it's usually an indication that when the credits
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read that the executive producer and the star of the movie are one and the same
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the movie itself is nothing more than a star vehicle with the story/screenplay
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not being up to scratch <br /><br />PROTOCOL follows the saga of one Sunny Davis
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a kooky bimboesque cocktail waitress who saves a visiting dignitary and as a reward
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gets made a top diplomat . Likely ? As things progress Ms Davis ( Who has problems
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being able to string two sentences together ) finds herself in more outlandish
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and less likely situations . When I say that PROTOCOL stars Goldie Hawn who is
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also the film's executive producer do you understand what I'm saying about the
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story/screenplay not being up to scratch ? Exactly
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- text: I've seen all four of the movies in this series. Each one strays further and
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further from the books. This is the worst one yet. My problem is that it does
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not follow the book it is titled after in any way! The directors and producers
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should have named it any thing other than "Love's Abiding Joy." The only thing
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about this movie that remotely resembles the book are the names of some of the
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characters (Willie, Missie, Henry, Clark, Scottie and Cookie). The names/ages/genders
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of the children are wrong. The entire story line is no where in the book.<br /><br
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/>I find it a great disservice to Janette Oke, her books and her fans to produce
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a movie under her title that is not correct in any way. The music is too loud.
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The actors are not convincing - they lack emotions.<br /><br />If you want a good
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family movie, this might do. It is clean. Don't watch it, though, if you are hoping
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for a condensed version of the book. I hope that this will be the last movie from
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this series, but I doubt it. If there are more movies made, I wish Michael Landon,
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Jr and others would stick closer to the original plot and story lines. The books
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are excellent and, if closely followed, would make excellent movies!
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- text: 'THE ZOMBIE CHRONICLES <br /><br />Aspect ratio: 1.33:1 (Nu-View 3-D)<br /><br
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/>Sound format: Mono<br /><br />Whilst searching for a (literal) ghost town in
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the middle of nowhere, a young reporter (Emmy Smith) picks up a grizzled hitchhiker
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(Joseph Haggerty) who tells her two stories involving flesh-eating zombies reputed
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to haunt the area.<br /><br />An ABSOLUTE waste of time, hobbled from the outset
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by Haggerty''s painfully amateurish performance in a key role. Worse still, the
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two stories which make up the bulk of the running time are utterly routine, made
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worse by indifferent performances and lackluster direction by Brad Sykes, previously
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responsible for the likes of CAMP BLOOD (1999). This isn''t a ''fun'' movie in
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the sense that Ed Wood''s movies are ''fun'' (he, at least, believed in what he
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was doing and was sincere in his efforts, despite a lack of talent); Sykes'' home-made
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movies are, in fact, aggravating, boring and almost completely devoid of any redeeming
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virtue, and most viewers will feel justifiably angry and cheated by such unimaginative,
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badly-conceived junk. The 3-D format is utterly wasted here.'
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- text: If only to avoid making this type of film in the future. This film is interesting
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as an experiment but tells no cogent story.<br /><br />One might feel virtuous
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for sitting thru it because it touches on so many IMPORTANT issues but it does
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so without any discernable motive. The viewer comes away with no new perspectives
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(unless one comes up with one while one's mind wanders, as it will invariably
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do during this pointless film).<br /><br />One might better spend one's time staring
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out a window at a tree growing.<br /><br />
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- text: Sexo Cannibal, or Devil Hunter as it's more commonly known amongst English
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speaking audiences, starts with actress & model Laura Crawford (Ursula Buchfellner
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as Ursula Fellner) checking out locations for her new film along with her assistant
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Jane (Gisela Hahn). After a long days work Laura is relaxing in the bath of her
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room when two very dubious character's named Chris (Werner Pochath) & Thomas (Antonio
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Mayans) burst in & kidnap her having been helped by the treacherous Jane. Laura's
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agent gets on the blower to rent-a-hero Peter Weston (Al Cliver) who is informed
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of the situation, the kidnappers have Laura on an isolated island & are demanding
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a 6 million ransom. Peter is told that he will be paid 200,000 to get her back
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safely & a further 10% of the 6 million if he brings that back as well, faster
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than a rat up a drain pipe Peter & his Vietnam Vet buddy helicopter pilot Jack
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are on the island & deciding on how to save Laura. So, the kidnappers have Laura
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& Peter has the 6 million but neither want to hand them over that much. Just to
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complicate things further this particular isolated island is home to a primitive
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tribe (hell, in all the generations they've lived there they've only managed to
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build one straw hut, now that's primitive) who worship some cannibal monster dude
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(Burt Altman) with bulging eyes as a God with human sacrifices & this cannibal
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has a liking for young, white female flesh & intestines...<br /><br />This Spanish,
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French & German co-production was co-written & directed by the prolific Jesus
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Franco who also gets the credit for the music as well. Sexo Cannibal has gained
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a certain amount of notoriety here in the UK as it was placed on the 'Video Nasties'
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list in the early 80's under it's alternate Devil Hunter title & therefore officially
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classed as obscene & banned, having said that I have no idea why as it is one
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bad film & even Franco, who isn't afraid to be associated with a turkey, decides
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he wants to hide under the pseudonym of Clifford Brown. I'd imagine even the most
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die-hard Franco fan would have a hard time defending this thing. The script by
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Franco, erm sorry I mean Clifford Brown & Julian Esteban as Julius Valery who
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was obviously another one less than impressed with the finished product & wanted
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his named removed, is awful. It's as simple & straight forward as that. For a
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start the film is so boring it's untrue, the kidnap plot is one of the dullest
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I've ever seen without the slightest bit of tension or excitement involved & the
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horror side of things don't improve as we get a big black guy with stupid looking
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over-sized bloodshot eyes plus two tame cannibal scenes. As a horror film Sexo
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Cannibal fails & as an action adventure it has no more success, this is one to
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avoid.<br /><br />Director Franco shows his usual incompetence throughout, a decapitated
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head is achieved by an actor lying on the ground with large leaves placed around
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the bottom of his neck to try & give the impression it's not attached to anything!
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The cannibal scenes are poor, the action is lame & it has endless scenes of people
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randomly walking around the jungle getting from 'A' to 'B' & not really doing
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anything when they get there either. It becomes incredibly dull & tedious to watch
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after about 10 minutes & don't forget this thing goes on for 94 minutes in it's
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uncut state. I also must mention the hilarious scene when Al Cliver is supposed
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to be climbing a cliff, this is achieved by Franco turning his camera on it's
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side & having Cliver crawl along the floor! Just look at the way his coat hangs
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& the way he never grabs onto to anything as he just pulls himself along! The
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gore isn't that great & as far as Euro cannibal films go this is very tame, there
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are some gross close ups of the cannibals mouth as it chews bits of meat, a man
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is impaled on spikes, there's some blood & a handful of intestines. There's a
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fair bit of nudity in Sexo Cannibal & an unpleasant rape scene.<br /><br />Sexo
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Cannibal must have had a low budget & I mean low. This is a shoddy poorly made
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film with awful special effects & rock bottom production values. The only decent
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thing about it is the jungle setting which at least looks authentic. The music
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sucks & sound effects become annoying as there is lots of heavy breathing whenever
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the cannibal is on screen. The acting sucks, the whole thing was obviously dubbed
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anyway but no one in this thing can act.<br /><br />Sexo Cannibal is a terrible
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film that commits the fatal mistake of being as boring as hell. The only good
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things I can say is that it has a certain sleazy atmosphere to it & those close
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ups of the cannibal chewing meat are pretty gross. Anyone looking for a decent
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cinematic experience should give Sexo Cannibal as wide a berth as possible, one
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to avoid.
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inference: true
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model-index:
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- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
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results:
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- task:
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type: text-classification
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name: Text Classification
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dataset:
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name: stanfordnlp/imdb
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type: stanfordnlp/imdb
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split: test
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metrics:
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- type: accuracy
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value: 0.8242
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name: Accuracy
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---
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# SetFit with sentence-transformers/paraphrase-mpnet-base-v2
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This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [stanfordnlp/imdb](https://huggingface.co/datasets/stanfordnlp/imdb) dataset that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
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The model has been trained using an efficient few-shot learning technique that involves:
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1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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2. Training a classification head with features from the fine-tuned Sentence Transformer.
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## Model Details
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### Model Description
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- **Model Type:** SetFit
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- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
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- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
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- **Maximum Sequence Length:** 512 tokens
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- **Number of Classes:** 2 classes
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- **Training Dataset:** [stanfordnlp/imdb](https://huggingface.co/datasets/stanfordnlp/imdb)
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<!-- - **Language:** Unknown -->
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<!-- - **License:** Unknown -->
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### Model Sources
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- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
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- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
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- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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### Model Labels
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| Label | Examples |
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+
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| 0 | <ul><li>"I can't believe that those praising this movie herein aren't thinking of some other film. I was prepared for the possibility that this would be awful, but the script (or lack thereof) makes for a film that's also pointless. On the plus side, the general level of craft on the part of the actors and technical crew is quite competent, but when you've got a sow's ear to work with you can't make a silk purse. Ben G fans should stick with just about any other movie he's been in. Dorothy S fans should stick to Galaxina. Peter B fans should stick to Last Picture Show and Target. Fans of cheap laughs at the expense of those who seem to be asking for it should stick to Peter B's amazingly awful book, Killing of the Unicorn."</li><li>'This has to be the worst piece of garbage I\'ve seen in a while.<br /><br />Heath Ledger is a heartthrob? He looked deformed. I wish I\'d known that he and Naomi Watts are an item in real life because I spent 2 of the longest hours of my life wondering what she saw in him. <br /><br />Orlando Bloom is a heartthrob? With the scraggly beard and deer-in-the-headlights look about him, I can\'t say I agree.<br /><br />Rachel Griffiths was her usual fabulous self, but Geoffrey Rush looked as if he couldn\'t wait to get off the set. <br /><br />I\'m supposed to feel sorry for bankrobbers and murderers? This is a far cry from Butch Cassidy, which actually WAS an entertaining film. This was trite, cliche-ridden and boring. We only stayed because we were convinced it would get better. It didn\'t.<br /><br />The last 10-15 minutes or so were unintentionally hilarious. Heath and his gang are holed up in a frontier hotel, and women and children are dying because of their presence. That\'s not funny. But it was funny when they walked out of the hotel with the armor on, because all we could think of was the Black Knight from Monty Python and the Holy Grail. I kept waiting for them to say "I\'ll bite yer leg off!" We were howling with laughter, as were several other warped members of the audience. When we left, pretty much everyone was talking about what a waste of time this film was.<br /><br />I may not have paid cash to see this disaster (sneak preview), but it certainly wasn\'t free. It cost me 2 hours of my life that I will never get back.'</li><li>"This movie was awful. The ending was absolutely horrible. There was no plot to the movie whatsoever. The only thing that was decent about the movie was the acting done by Robert DuVall and James Earl Jones. Their performances were excellent! The only problem was that the movie did not do their acting performances any justice. If the script would have come close to capturing a halfway decent story, it would be worth watching. Instead, Robert DuVall's and James Earl Jones' performances are completely wasted on a god awful storyline...or lack thereof. Not only was I left waiting throughout the movie for something to happen to make the movie....well an actual movie...not just utterless dialog between characters for what ended up being absolutely no reason. It was nothing more than common dialog that would have taken place back in that period of time. There was nothing special about any of the characters. The only thing special was how Robert DuVall portrayed a rambling, senile, drunk, old man. Nothing worthy happens during the entire movie including the end. When the movie ended, I sat amazed...amazed that I sat through the entire movie waiting for something of interest to happen to make watching the movie worth while. It never happened! The cast of characters suddenly started rolling making it apparent that the movie really was over and I realized that I had just wasted 2 hours of my life watching a movie with absolutely no plot and no meaning. It wasn't even a story. The entire movie takes place in a day's worth of time. That's it. It was one day in the life (and death) of some Southerners on a plantation. How much of a story can take place in a single day (other than the movie Training Day)? The acting performances by the entire cast were excellent, but they were grossly wasted on such a disappointment of a movie...if you can even call it a movie."</li></ul> |
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| 1 | <ul><li>"OK its not the best film I've ever seen but at the same time I've been able to sit and watch it TWICE!!! story line was pretty awful and during the first part of the first short story i wondered what the hell i was watching but at the same time it was so awful i loved it cheap laughs all the way.<br /><br />And Jebidia deserves an Oscar for his role in this movie the only thing that let him down was half way through he stopped his silly name calling.<br /><br />overall the film was pretty perfetic but if your after cheap laughs and you see it in pound land go by it."</li><li>"I very much looked forward to this movie. Its a good family movie; however, if Michael Landon Jr.'s editing team did a better job of editing, the movie would be much better. Too many scenes out of context. I do hope there is another movie from the series, they're all very good. But, if another one is made, I beg them to take better care at editing. This story was all over the place and didn't seem to have a center. Which is unfortunate because the other movies of the series were great. I enjoy the story of Willie and Missy; they're both great role models. Plus, the romantic side of the viewers always enjoy a good love story."</li><li>"or anyone who was praying for the sight of Al Cliver wrestling a naked, 7ft tall black guy into a full nelson, your film has arrived! Film starlet Laura Crawford (Ursula Buchfellner) is kidnapped by a group who demand the ransom of $6 million to be delivered to their island hideaway. What they don't count on is rugged Vietnam vet Peter Weston (Cliver) being hired by a film producer to save the girl. And what they really didn't count on was a local tribe that likes to offer up young women to their monster cannibal god with bloodshot bug eyes.<br /><br />Pretty much the same filming set up as CANNIBALS, this one fares a bit better when it comes to entertainment value, thanks mostly a hilarious dub track and the impossibly goofy monster with the bulging eyes (Franco confirms they were split ping pong balls on the disc's interview). Franco gets a strong EuroCult supporting cast including Gisela Hahn (CONTAMINATION) and Werner Pochath (whose death is one of the most head-scratching things I ever seen as a guy who is totally not him is shown - in close up - trying to be him). The film features tons of nudity and the gore (Tempra paint variety) is there. The highlight for me was the world's slowly fistfight between Cliver and Antonio de Cabo in the splashing waves. Sadly, ol' Jess pads this one out to an astonishing (and, at times, agonizing) 1 hour and 40 minutes when it should have run 80 minutes tops. <br /><br />For the most part, the Severin DVD looks pretty nice but there are some odd ghosting images going on during some of the darker scenes. Also, one long section of dialog is in Spanish with no subs (they are an option, but only when you listen to the French track). Franco gives a nice 16- minute interview about the film and has much more pleasant things to say about Buchfellner than his CANNIBALS star Sabrina Siani."</li></ul> |
|
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+
|
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+
## Evaluation
|
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+
|
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+
### Metrics
|
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+
| Label | Accuracy |
|
181 |
+
|:--------|:---------|
|
182 |
+
| **all** | 0.8242 |
|
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+
|
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+
## Uses
|
185 |
+
|
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+
### Direct Use for Inference
|
187 |
+
|
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+
First install the SetFit library:
|
189 |
+
|
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+
```bash
|
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+
pip install setfit
|
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+
```
|
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+
|
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+
Then you can load this model and run inference.
|
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+
|
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+
```python
|
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+
from setfit import SetFitModel
|
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+
|
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+
# Download from the 🤗 Hub
|
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+
model = SetFitModel.from_pretrained("setfit_model_id")
|
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+
# Run inference
|
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+
preds = model("If only to avoid making this type of film in the future. This film is interesting as an experiment but tells no cogent story.<br /><br />One might feel virtuous for sitting thru it because it touches on so many IMPORTANT issues but it does so without any discernable motive. The viewer comes away with no new perspectives (unless one comes up with one while one's mind wanders, as it will invariably do during this pointless film).<br /><br />One might better spend one's time staring out a window at a tree growing.<br /><br />")
|
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+
```
|
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+
|
205 |
+
<!--
|
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+
### Downstream Use
|
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+
|
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+
*List how someone could finetune this model on their own dataset.*
|
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+
-->
|
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+
|
211 |
+
<!--
|
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+
### Out-of-Scope Use
|
213 |
+
|
214 |
+
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
|
215 |
+
-->
|
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+
|
217 |
+
<!--
|
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+
## Bias, Risks and Limitations
|
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+
|
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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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+
|
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+
<!--
|
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+
### Recommendations
|
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+
|
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+
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
|
227 |
+
-->
|
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+
|
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+
## Training Details
|
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+
|
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+
### Training Set Metrics
|
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+
| Training set | Min | Median | Max |
|
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+
|:-------------|:----|:---------|:----|
|
234 |
+
| Word count | 48 | 244.4571 | 888 |
|
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+
|
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+
| Label | Training Sample Count |
|
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+
|:------|:----------------------|
|
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+
| 1 | 7 |
|
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+
| 0 | 63 |
|
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+
|
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+
### Training Hyperparameters
|
242 |
+
- batch_size: (16, 16)
|
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+
- num_epochs: (1, 1)
|
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+
- max_steps: -1
|
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+
- sampling_strategy: oversampling
|
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+
- body_learning_rate: (2e-05, 1e-05)
|
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+
- head_learning_rate: 0.01
|
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+
- loss: CosineSimilarityLoss
|
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+
- distance_metric: cosine_distance
|
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+
- margin: 0.25
|
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+
- end_to_end: False
|
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+
- use_amp: False
|
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+
- warmup_proportion: 0.1
|
254 |
+
- seed: 42
|
255 |
+
- eval_max_steps: -1
|
256 |
+
- load_best_model_at_end: True
|
257 |
+
|
258 |
+
### Training Results
|
259 |
+
| Epoch | Step | Training Loss | Validation Loss |
|
260 |
+
|:-------:|:-------:|:-------------:|:---------------:|
|
261 |
+
| 0.0039 | 1 | 0.2493 | - |
|
262 |
+
| 0.1953 | 50 | 0.0016 | - |
|
263 |
+
| 0.3906 | 100 | 0.0003 | - |
|
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+
| 0.5859 | 150 | 0.003 | - |
|
265 |
+
| 0.7812 | 200 | 0.0014 | - |
|
266 |
+
| 0.9766 | 250 | 0.0002 | - |
|
267 |
+
| **1.0** | **256** | **-** | **0.4699** |
|
268 |
+
|
269 |
+
* The bold row denotes the saved checkpoint.
|
270 |
+
### Framework Versions
|
271 |
+
- Python: 3.8.19
|
272 |
+
- SetFit: 1.0.3
|
273 |
+
- Sentence Transformers: 3.0.1
|
274 |
+
- Transformers: 4.39.0
|
275 |
+
- PyTorch: 2.4.0
|
276 |
+
- Datasets: 2.20.0
|
277 |
+
- Tokenizers: 0.15.2
|
278 |
+
|
279 |
+
## Citation
|
280 |
+
|
281 |
+
### BibTeX
|
282 |
+
```bibtex
|
283 |
+
@article{https://doi.org/10.48550/arxiv.2209.11055,
|
284 |
+
doi = {10.48550/ARXIV.2209.11055},
|
285 |
+
url = {https://arxiv.org/abs/2209.11055},
|
286 |
+
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
|
287 |
+
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
|
288 |
+
title = {Efficient Few-Shot Learning Without Prompts},
|
289 |
+
publisher = {arXiv},
|
290 |
+
year = {2022},
|
291 |
+
copyright = {Creative Commons Attribution 4.0 International}
|
292 |
+
}
|
293 |
+
```
|
294 |
+
|
295 |
+
<!--
|
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+
## Glossary
|
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+
|
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+
*Clearly define terms in order to be accessible across audiences.*
|
299 |
+
-->
|
300 |
+
|
301 |
+
<!--
|
302 |
+
## Model Card Authors
|
303 |
+
|
304 |
+
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
305 |
+
-->
|
306 |
+
|
307 |
+
<!--
|
308 |
+
## Model Card Contact
|
309 |
+
|
310 |
+
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
|
311 |
+
-->
|
config.json
ADDED
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|
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{
|
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+
"_name_or_path": "setfit/step_256",
|
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+
"architectures": [
|
4 |
+
"MPNetModel"
|
5 |
+
],
|
6 |
+
"attention_probs_dropout_prob": 0.1,
|
7 |
+
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|
8 |
+
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|
9 |
+
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|
10 |
+
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|
11 |
+
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|
12 |
+
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|
13 |
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|
14 |
+
"layer_norm_eps": 1e-05,
|
15 |
+
"max_position_embeddings": 514,
|
16 |
+
"model_type": "mpnet",
|
17 |
+
"num_attention_heads": 12,
|
18 |
+
"num_hidden_layers": 12,
|
19 |
+
"pad_token_id": 1,
|
20 |
+
"relative_attention_num_buckets": 32,
|
21 |
+
"torch_dtype": "float32",
|
22 |
+
"transformers_version": "4.39.0",
|
23 |
+
"vocab_size": 30527
|
24 |
+
}
|
config_sentence_transformers.json
ADDED
@@ -0,0 +1,10 @@
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|
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+
{
|
2 |
+
"__version__": {
|
3 |
+
"sentence_transformers": "3.0.1",
|
4 |
+
"transformers": "4.39.0",
|
5 |
+
"pytorch": "2.4.0"
|
6 |
+
},
|
7 |
+
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|
8 |
+
"default_prompt_name": null,
|
9 |
+
"similarity_fn_name": null
|
10 |
+
}
|
config_setfit.json
ADDED
@@ -0,0 +1,7 @@
|
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|
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+
{
|
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+
"labels": [
|
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+
"1",
|
4 |
+
"0"
|
5 |
+
],
|
6 |
+
"normalize_embeddings": false
|
7 |
+
}
|
model.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:a246a9bff2a49daa7c720648bb2fe7731371fef61173c5fe2b39d3e834768cf1
|
3 |
+
size 437967672
|
model_head.pkl
ADDED
@@ -0,0 +1,3 @@
|
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|
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|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:acf495d66eebbc7f9587d2db39b675ca2f27db50f59f36583889482de74df204
|
3 |
+
size 6991
|
modules.json
ADDED
@@ -0,0 +1,14 @@
|
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|
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+
[
|
2 |
+
{
|
3 |
+
"idx": 0,
|
4 |
+
"name": "0",
|
5 |
+
"path": "",
|
6 |
+
"type": "sentence_transformers.models.Transformer"
|
7 |
+
},
|
8 |
+
{
|
9 |
+
"idx": 1,
|
10 |
+
"name": "1",
|
11 |
+
"path": "1_Pooling",
|
12 |
+
"type": "sentence_transformers.models.Pooling"
|
13 |
+
}
|
14 |
+
]
|
sentence_bert_config.json
ADDED
@@ -0,0 +1,4 @@
|
|
|
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|
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|
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|
|
|
1 |
+
{
|
2 |
+
"max_seq_length": 512,
|
3 |
+
"do_lower_case": false
|
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+
}
|
special_tokens_map.json
ADDED
@@ -0,0 +1,51 @@
|
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+
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|
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|
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|
4 |
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|
5 |
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|
6 |
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|
7 |
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|
8 |
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|
9 |
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|
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+
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|
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|
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|
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|
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|
15 |
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},
|
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|
17 |
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|
18 |
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|
19 |
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|
20 |
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|
21 |
+
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|
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+
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|
23 |
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|
24 |
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|
25 |
+
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|
26 |
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|
27 |
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|
28 |
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|
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+
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|
30 |
+
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|
31 |
+
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|
32 |
+
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|
33 |
+
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|
34 |
+
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|
35 |
+
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|
36 |
+
},
|
37 |
+
"sep_token": {
|
38 |
+
"content": "</s>",
|
39 |
+
"lstrip": false,
|
40 |
+
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|
41 |
+
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|
42 |
+
"single_word": false
|
43 |
+
},
|
44 |
+
"unk_token": {
|
45 |
+
"content": "[UNK]",
|
46 |
+
"lstrip": false,
|
47 |
+
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|
48 |
+
"rstrip": false,
|
49 |
+
"single_word": false
|
50 |
+
}
|
51 |
+
}
|
tokenizer.json
ADDED
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tokenizer_config.json
ADDED
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|
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+
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|
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|
3 |
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|
4 |
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|
5 |
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|
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|
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|
8 |
+
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|
9 |
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"special": true
|
10 |
+
},
|
11 |
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"1": {
|
12 |
+
"content": "<pad>",
|
13 |
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|
14 |
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|
15 |
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|
16 |
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|
17 |
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|
18 |
+
},
|
19 |
+
"2": {
|
20 |
+
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|
21 |
+
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|
22 |
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|
23 |
+
"rstrip": false,
|
24 |
+
"single_word": false,
|
25 |
+
"special": true
|
26 |
+
},
|
27 |
+
"104": {
|
28 |
+
"content": "[UNK]",
|
29 |
+
"lstrip": false,
|
30 |
+
"normalized": false,
|
31 |
+
"rstrip": false,
|
32 |
+
"single_word": false,
|
33 |
+
"special": true
|
34 |
+
},
|
35 |
+
"30526": {
|
36 |
+
"content": "<mask>",
|
37 |
+
"lstrip": true,
|
38 |
+
"normalized": false,
|
39 |
+
"rstrip": false,
|
40 |
+
"single_word": false,
|
41 |
+
"special": true
|
42 |
+
}
|
43 |
+
},
|
44 |
+
"bos_token": "<s>",
|
45 |
+
"clean_up_tokenization_spaces": true,
|
46 |
+
"cls_token": "<s>",
|
47 |
+
"do_basic_tokenize": true,
|
48 |
+
"do_lower_case": true,
|
49 |
+
"eos_token": "</s>",
|
50 |
+
"mask_token": "<mask>",
|
51 |
+
"max_length": 512,
|
52 |
+
"model_max_length": 512,
|
53 |
+
"never_split": null,
|
54 |
+
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|
55 |
+
"pad_token": "<pad>",
|
56 |
+
"pad_token_type_id": 0,
|
57 |
+
"padding_side": "right",
|
58 |
+
"sep_token": "</s>",
|
59 |
+
"stride": 0,
|
60 |
+
"strip_accents": null,
|
61 |
+
"tokenize_chinese_chars": true,
|
62 |
+
"tokenizer_class": "MPNetTokenizer",
|
63 |
+
"truncation_side": "right",
|
64 |
+
"truncation_strategy": "longest_first",
|
65 |
+
"unk_token": "[UNK]"
|
66 |
+
}
|
vocab.txt
ADDED
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|
|