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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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- dense |
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- generated_from_trainer |
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- dataset_size:11180 |
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- loss:CosineSimilarityLoss |
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widget: |
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- source_sentence: So when are they leaving? I saw the protest had smaller amounts |
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of protestors today as opposed to Friday and Saturday |
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sentences: |
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- Hillary's great, but center-leftists seem to do better with younger candidates. |
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Bill, Blair, Obama, Trudeau, and now Macron. She'll be 72 in 2020. |
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- . I felt very sorry for you during your meltdown on He drove you insane but, |
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of course, Piers is a lot smarter than you |
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- As a kid, my friends and I all believed that Gymkata was the most violent, bloody |
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movie ever made. I'm not sure who started that rumor. It was probably born out |
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of the frustration of 10 year olds who weren't allowed to see it for one reason |
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or other. Years after Gymkata was released, it became a perennial late night cable |
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movie, and as a result, I've been able to make up for lost time. I must have seen |
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scenes from this dreadful excuse for a film over a dozen times, and I can always |
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spot it from 1-2 seconds of screen time. However, aside from the forced coupling |
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of gymnastics and martial arts, the bad dubbing, the stiff dialog, and the outrageously |
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difficult story-line, the film has some things going for it. With all that's bad |
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about the movie visually, the sound is actually pretty entertaining. Never before |
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has a punch or kick landed with so little force and so much volume! The canned |
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kung-fu sounds are cheeky, but the slowed and pitched-down music, and the nearly |
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5 minute slow motion scene are truly weird. The chase through the city of demented, |
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blood-thirsty villagers isn't really tense as much as it is irritating, and there |
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are enough bad wigs and extras who all but look into the camera and wave to make |
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this train-wreck a little fun. Could it be headed for cult-classic status? Where |
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is MST3K when we need it? |
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- source_sentence: Seriously, 3 things that really get my blood boiling is hearing |
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about child, animal and/or elder abuse. There aint much worse than worthless fucks |
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who prey on those in our society who cannot defend themselves. Worthless people |
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deserve nothing more than a rope around the neck or life in prison at the very |
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least. |
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sentences: |
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- As elsewhere, we see polling places and campaign offices getting attacked by& |
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checks notes again& oh yeah, also republicans. Yeah, that checks out. |
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- Biometric ID System Said to Delay Venezuela Recall By CHRISTOPHER TOOTHAKER CARACAS, |
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Venezuela (AP) -- The high-tech thumbprint devices were meant to keep people from |
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voting more than once in the recall ballot against President Hugo Chavez. Instead, |
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they often wound up working fitfully - even when Chavez himself voted - contributing |
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to huge delays in Sunday's historic referendum... |
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- Assuming a republican controlled senate (likely), he will replace Alito and Thomas. |
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It won't necessarily move *further* to the right, but we'll be stuck with at least |
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5 conservative justices for the next 40 years. |
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- source_sentence: i should love this movie . the acting is very good and Barbara |
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Stanwyck is great but the the movie has always seemed very trite to me . the movie |
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makes working class people look low and cheap .the fact that the daughter is ashamed |
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of her mother and that the daughter does not rise above it has always made me |
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a bit uneasy . Barbara Stanwyck as the mother worships the daughter but the daughter |
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forgoes a mothers love to find happiness with her well to do fathers family . |
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i wonder how many others who have seen this film feel this way about it.again |
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the acting was very very good and worth watching . i really don't like the story |
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line . just a personal preference .thank you |
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sentences: |
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- god ..it takes me back...rolling skating at roller gardens,,,,,you cant top old |
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school...the beats back then were so much better... |
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- 'I''m glad I love my military and the 2nd amendment. #2A #Marines #tlot #USA' |
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- We definitely need someone better than Trump in 2024, but for now he's all we |
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got.. |
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- source_sentence: That's just because his right arm is on the inside. Trump knows |
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there's nothing he can do to win this round, and he's okay with that. Trump is |
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well versed in handshake game strategy, as is Macron clearly. |
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sentences: |
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- "By Theo Burman - Live News Reporter: \n\nFormer President Donald Trump and Vice\ |
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\ President Kamala Harris are in the final sprint to the finishing line in their\ |
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\ race to the White House. There are 12 days until Election Day and both campaigns\ |
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\ are working flat-out to win over voters in what is shaping up to be one of the\ |
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\ closest presidential elections in modern history.\n\nAfter events in North Carolina\ |
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\ and Georgia earlier this week, Trump is continuing his focus on the Sun Belt\ |
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\ by heading to Arizona today, while Harris is hosting a rally alongside former\ |
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\ President Barack Obama and Bruce Springsteen in Georgia.\n\nRead more: [" |
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- '"😂😂😂😂😂😂😂😂😂😂 |
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Gay niggas couldn''t wait to act like bitches tonight"' |
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- When the regressive figure has tremendous power (such as a head of state) it's |
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usually not worth risking diplomatic friendship to refuse a rather small thing |
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such as wearing a hijab. Le Pen is making wearing a hijab a big thing to appeal |
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to both anti-Islam and feminism emotions, in other words, risking diplomatic friendship |
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to boost her own popularity. Nice move overall. |
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- source_sentence: Unfortunately, the angry masses demand what's not in their best |
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interest because of brown people |
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sentences: |
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- 'If Le Pen is perceived to be a US-puppet, wouldn''t that rub a lot of patriotic/nationalistic |
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voters the wrong way? |
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It doesn''t seem to be a problem for Trumpists that acknowledge his close ties |
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(sic) with Putin.' |
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- 'I made it 22 years. #metoo' |
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- 'Secondly, every major support has been leaving the boat during the campaign to |
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be in Macron team, thus leaving Hamon alone in an already very fragile party. |
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Sounds like they made quite a ripple' |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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metrics: |
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- pearson_cosine |
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- spearman_cosine |
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model-index: |
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- name: SentenceTransformer |
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results: |
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- task: |
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type: semantic-similarity |
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name: Semantic Similarity |
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dataset: |
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name: similarity |
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type: similarity |
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metrics: |
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- type: pearson_cosine |
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value: 0.3952284283585713 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.41014481263817126 |
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name: Spearman Cosine |
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--- |
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# SentenceTransformer |
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This is a [sentence-transformers](https://www.SBERT.net) model trained. 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:** [Unknown](https://huggingface.co/unknown) --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 1024 dimensions |
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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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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'RobertaModel'}) |
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(1): 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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) |
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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("sentence_transformers_model_id") |
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# Run inference |
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sentences = [ |
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"Unfortunately, the angry masses demand what's not in their best interest because of brown people", |
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'I made it 22 years. #metoo', |
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"If Le Pen is perceived to be a US-puppet, wouldn't that rub a lot of patriotic/nationalistic voters the wrong way?\n\nIt doesn't seem to be a problem for Trumpists that acknowledge his close ties (sic) with Putin.", |
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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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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities) |
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# tensor([[1.0000, 0.6501, 0.5940], |
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# [0.6501, 1.0000, 0.5664], |
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# [0.5940, 0.5664, 1.0000]]) |
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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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<!-- |
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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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## Evaluation |
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### Metrics |
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#### Semantic Similarity |
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* Dataset: `similarity` |
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* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
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| Metric | Value | |
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|:--------------------|:-----------| |
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| pearson_cosine | 0.3952 | |
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| **spearman_cosine** | **0.4101** | |
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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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<!-- |
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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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--> |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 11,180 training samples |
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* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence_0 | sentence_1 | label | |
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|:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:---------------------------------------------------------------| |
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| type | string | string | float | |
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| details | <ul><li>min: 5 tokens</li><li>mean: 102.41 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 111.27 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.53</li><li>max: 1.0</li></ul> | |
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* Samples: |
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| sentence_0 | sentence_1 | label | |
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|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------| |
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| <code>We love peace, but not peace at any price.</code> | <code>That's totally not corrupt whatsoever. Also why the hell is a state attorney general meddling in federal government?</code> | <code>0.7071067811865475</code> | |
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| <code>Am not from America, I usually watch this show on AXN channel, I don't know why this respected channel air such sucking program in prime time slot. Creation of Hollywood's Money Bank Jerry Bruckheimer, this time he is spending a big load of cash in the small screen. In each episode a bunch of peoples having two team members travels from on country to another for a great sum of money; where the camera crews shoot their travels. I don't know who the hell gave this stupid idea for the show. It has nothing to watch for, in all episodes we see people ran like beggars, some times shouting, crying, beeping, jerky camera works..huh it's harmful to both eyes and ears. The most disgusting part in the race is the viewers finally knows each of the team members can't enjoy their race/traveling experience. Even though, to add up the ratings the producers came up with the ideas of including Gays in one shows, sucking American reality show.It's nothing to watch for, better switch to another channels.T...</code> | <code>Background: Last year my [41F] brother, Gabe [36M] came to visit around my bday. There is a nice restaurant my family goes to for special occasions, and since Gabe is a chef, I was excited to take him. I made a rez for me, my SO, my kids [23NB, 21F], Gabe, and my sister, Ronnie [35F]. We had a great time. It was "adults only," so my nephews [15, 13] did not come. Since I invited them, we paid; the bill was about $400.<br><br>Gabe came to visit again in Sept, only stopping for a few days (arrived Sun eve, leaving early Wed am), on his way back home across the country. Asking if he wanted to do anything while in town, he said he'd like to go to that restaurant again. When we saw Ronnie (Sunday), I told her we were going "and you are coming with us."<br><br>Monday, I took the day off to hang out with Gabe, my sis had to work, but she didn't come over when she got off at 7pm.<br><br>Tuesday she came over with my nephews around 11am, with dinner rez for 6 ppl (same as last time) at 8pm. We hung out and as th...</code> | <code>0.3535533905932737</code> | |
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| <code>I (M29) am trans. My girlfriend (F28, GF) is totally cool with it, always has been, we've been dating since college, 8 years in March. <br><br>GF's dad was abusive, so she left home at 18 and had to leave her baby sister behind.<br><br>2015, we're 24/23, in grad school, living together. GF gets some news: her dad died and, long story short, nobody can take her sister in.<br><br>We hire a lawyer to try for custody. I quit school to work fulltime so we can afford it. It takes a lot of time and work, but we get to take her home.<br><br>Fast forward to now. Kid (12, S) has school in person on Tu/Th, virtual learning the rest. Friday the 11th, while S was out walking the dog, I grabbed the hamper out of their room to do the laundry. The pocket of the hoodie they just wore to school was bunched up weird, so I checked it and pulled out a binder. <br><br>For those who don't know, a binder is usually used by trans people to flatten their chests so they can pass easier. The only other reason I could think of for someone to o...</code> | <code>Scores plan to leave Mormon church over its policy on same-sex couples - Gay Star News</code> | <code>0.4082482904638631</code> | |
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* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: |
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```json |
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{ |
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"loss_fct": "torch.nn.modules.loss.MSELoss" |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 32 |
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- `fp16`: True |
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- `multi_dataset_batch_sampler`: round_robin |
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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`: steps |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 32 |
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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 |
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- `num_train_epochs`: 3 |
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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`: True |
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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`: None |
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- `hub_always_push`: False |
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- `hub_revision`: None |
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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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- `include_for_metrics`: [] |
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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 |
|
|
- `torch_compile_backend`: None |
|
|
- `torch_compile_mode`: None |
|
|
- `include_tokens_per_second`: False |
|
|
- `include_num_input_tokens_seen`: False |
|
|
- `neftune_noise_alpha`: None |
|
|
- `optim_target_modules`: None |
|
|
- `batch_eval_metrics`: False |
|
|
- `eval_on_start`: False |
|
|
- `use_liger_kernel`: False |
|
|
- `liger_kernel_config`: None |
|
|
- `eval_use_gather_object`: False |
|
|
- `average_tokens_across_devices`: False |
|
|
- `prompts`: None |
|
|
- `batch_sampler`: batch_sampler |
|
|
- `multi_dataset_batch_sampler`: round_robin |
|
|
- `router_mapping`: {} |
|
|
- `learning_rate_mapping`: {} |
|
|
|
|
|
</details> |
|
|
|
|
|
### Training Logs |
|
|
| Epoch | Step | Training Loss | similarity_spearman_cosine | |
|
|
|:------:|:----:|:-------------:|:--------------------------:| |
|
|
| 0.0286 | 10 | - | 0.0535 | |
|
|
| 0.0571 | 20 | - | 0.0570 | |
|
|
| 0.0857 | 30 | - | 0.0681 | |
|
|
| 0.1143 | 40 | - | 0.0739 | |
|
|
| 0.1429 | 50 | - | 0.0572 | |
|
|
| 0.1714 | 60 | - | 0.0250 | |
|
|
| 0.2 | 70 | - | 0.0230 | |
|
|
| 0.2286 | 80 | - | 0.0726 | |
|
|
| 0.2571 | 90 | - | 0.0548 | |
|
|
| 0.2857 | 100 | - | 0.0451 | |
|
|
| 0.3143 | 110 | - | 0.0067 | |
|
|
| 0.3429 | 120 | - | 0.0425 | |
|
|
| 0.3714 | 130 | - | 0.0920 | |
|
|
| 0.4 | 140 | - | 0.0823 | |
|
|
| 0.4286 | 150 | - | 0.1165 | |
|
|
| 0.4571 | 160 | - | 0.1405 | |
|
|
| 0.4857 | 170 | - | 0.1661 | |
|
|
| 0.5143 | 180 | - | 0.1657 | |
|
|
| 0.5429 | 190 | - | 0.1832 | |
|
|
| 0.5714 | 200 | - | 0.0056 | |
|
|
| 0.6 | 210 | - | 0.1209 | |
|
|
| 0.6286 | 220 | - | 0.1280 | |
|
|
| 0.6571 | 230 | - | 0.1902 | |
|
|
| 0.6857 | 240 | - | 0.2111 | |
|
|
| 0.7143 | 250 | - | 0.2717 | |
|
|
| 0.7429 | 260 | - | 0.2716 | |
|
|
| 0.7714 | 270 | - | 0.2629 | |
|
|
| 0.8 | 280 | - | 0.2171 | |
|
|
| 0.8286 | 290 | - | 0.2742 | |
|
|
| 0.8571 | 300 | - | 0.2913 | |
|
|
| 0.8857 | 310 | - | 0.2813 | |
|
|
| 0.9143 | 320 | - | 0.2863 | |
|
|
| 0.9429 | 330 | - | 0.2918 | |
|
|
| 0.9714 | 340 | - | 0.2951 | |
|
|
| 1.0 | 350 | - | 0.3198 | |
|
|
| 1.0286 | 360 | - | 0.3145 | |
|
|
| 1.0571 | 370 | - | 0.3148 | |
|
|
| 1.0857 | 380 | - | 0.2907 | |
|
|
| 1.1143 | 390 | - | 0.3267 | |
|
|
| 1.1429 | 400 | - | 0.3246 | |
|
|
| 1.1714 | 410 | - | 0.3351 | |
|
|
| 1.2 | 420 | - | 0.3463 | |
|
|
| 1.2286 | 430 | - | 0.3531 | |
|
|
| 1.2571 | 440 | - | 0.3398 | |
|
|
| 1.2857 | 450 | - | 0.3169 | |
|
|
| 1.3143 | 460 | - | 0.3304 | |
|
|
| 1.3429 | 470 | - | 0.3315 | |
|
|
| 1.3714 | 480 | - | 0.3684 | |
|
|
| 1.4 | 490 | - | 0.3499 | |
|
|
| 1.4286 | 500 | 0.1429 | 0.3438 | |
|
|
| 1.4571 | 510 | - | 0.3362 | |
|
|
| 1.4857 | 520 | - | 0.3130 | |
|
|
| 1.5143 | 530 | - | 0.3445 | |
|
|
| 1.5429 | 540 | - | 0.3464 | |
|
|
| 1.5714 | 550 | - | 0.3499 | |
|
|
| 1.6 | 560 | - | 0.3626 | |
|
|
| 1.6286 | 570 | - | 0.3743 | |
|
|
| 1.6571 | 580 | - | 0.3714 | |
|
|
| 1.6857 | 590 | - | 0.3774 | |
|
|
| 1.7143 | 600 | - | 0.3624 | |
|
|
| 1.7429 | 610 | - | 0.3861 | |
|
|
| 1.7714 | 620 | - | 0.3925 | |
|
|
| 1.8 | 630 | - | 0.3763 | |
|
|
| 1.8286 | 640 | - | 0.3906 | |
|
|
| 1.8571 | 650 | - | 0.4034 | |
|
|
| 1.8857 | 660 | - | 0.3887 | |
|
|
| 1.9143 | 670 | - | 0.3970 | |
|
|
| 1.9429 | 680 | - | 0.3787 | |
|
|
| 1.9714 | 690 | - | 0.3958 | |
|
|
| 2.0 | 700 | - | 0.3812 | |
|
|
| 2.0286 | 710 | - | 0.3951 | |
|
|
| 2.0571 | 720 | - | 0.4066 | |
|
|
| 2.0857 | 730 | - | 0.4030 | |
|
|
| 2.1143 | 740 | - | 0.4029 | |
|
|
| 2.1429 | 750 | - | 0.3899 | |
|
|
| 2.1714 | 760 | - | 0.3898 | |
|
|
| 2.2 | 770 | - | 0.3987 | |
|
|
| 2.2286 | 780 | - | 0.4007 | |
|
|
| 2.2571 | 790 | - | 0.4040 | |
|
|
| 2.2857 | 800 | - | 0.4101 | |
|
|
|
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.11.9 |
|
|
- Sentence Transformers: 5.1.0 |
|
|
- Transformers: 4.53.3 |
|
|
- PyTorch: 2.5.1 |
|
|
- Accelerate: 1.10.0 |
|
|
- Datasets: 2.14.4 |
|
|
- Tokenizers: 0.21.0 |
|
|
|
|
|
## Citation |
|
|
|
|
|
### BibTeX |
|
|
|
|
|
#### Sentence Transformers |
|
|
```bibtex |
|
|
@inproceedings{reimers-2019-sentence-bert, |
|
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
|
month = "11", |
|
|
year = "2019", |
|
|
publisher = "Association for Computational Linguistics", |
|
|
url = "https://arxiv.org/abs/1908.10084", |
|
|
} |
|
|
``` |
|
|
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