Add new SentenceTransformer model

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 1024,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

@@ -0,0 +1,533 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:11808
+- loss:Infonce
+base_model: BAAI/bge-m3
+widget:
+- source_sentence: Who are some notable individuals named Roger Mason
+  sentences:
+  - "Rav Kook's writings are extensive, and he is considered one of the most celebrated\
+    \ and influential rabbis of the 20th century. Some rabbis recommend that students\
+    \ of his begin studying his writings with Ein Ayah. References\n\nExternal links\n\
+    \ Ayin Ayah (full text), Hebrew Wikisource\n * Ayn Aya Classes in English\n\n\
+    Talmud\nAggadic Midrashim\nAbraham Isaac Kook\nHebrew-language religious books"
+  - 'Roger Mason may refer to:
+
+
+    Roger Mason (baseball) (born 1958), American baseball player
+
+    Roger Mason (geologist) (born 1941), discoverer of Ediacaran fossils
+
+    Roger Mason Jr. (born 1980), American basketball player
+
+    Roger Mason (musician), Australian keyboardist
+
+    L. Roger Mason, Jr., former assistant director of National Intelligence for Systems
+    and Resource Analyses'
+  - 'Timetabled passenger services on both lines had ceased by the end of February
+    1959. Shipping
+
+    The Bourne-Morton Canal or Bourne Old Eau connected the town to the sea in Roman
+    times. Until the mid-19th century, the present Bourne Eau was capable of carrying
+    commercial boat traffic from the Wash coast and Spalding. This resulted from the
+    investment following the Bourne Navigation Act of 1780. Passage became impossible
+    once the junction of the Eau and the River Glen was converted from gates to a
+    sluice in 1860. Media
+
+    Local news and television programmes are provided by BBC Yorkshire and Lincolnshire
+    and ITV Yorkshire. Television signals are received from the Belmont TV transmitter,
+    the Waltham TV transmitter can also be received which broadcast BBC East Midlands
+    and ITV Central programmes. Local radio stations are BBC Radio Lincolnshire, Greatest
+    Hits Radio Lincolnshire and Lincs FM. The town''s local newspapers are Bourne
+    Local  and Stamford Mercury. Sport
+
+    Bourne Town Football Club plays football in the United Counties Football League,
+    whilst Bourne Cricket Club plays in the Lincolnshire ECB Premier League. These
+    teams play their home games at the Abbey Lawn, a recreation ground privately owned
+    by the Bourne United Charities. Motor sports
+
+
+    The racing-car marques English Racing Automobiles (ERA) and British Racing Motors
+    (BRM) were both founded in Bourne by Raymond Mays, an international racing driver
+    and designer who lived in Bourne. The former ERA and BRM workshops in Spalding
+    Road are adjacent to Eastgate House, the Mays'' family home in the town''s Eastgate.
+    Landmarks
+
+
+    There are currently 71 listed buildings in the parish of Bourne, the most important
+    being Bourne Abbey and the Parish Church of St Peter and St Paul (1138), which
+    is the only one scheduled Grade I. Notable people
+
+    Bourne is reputedly the birthplace of Hereward the Wake (in about 1035), although
+    the 12th-century source of this information, De Gestis Herwardi Saxonis, refers
+    only to his father as being "of Bourne" and to the father''s house and retainers
+    there. Robert Mannyng (1264–1340) is credited with putting the speech of the ordinary
+    people of his time into recognisable form. He is better known as Robert de Brunne
+    because of his long period of residence as a canon at Bourne Abbey. There he completed
+    his life''s work of popularising religious and historical material in a Middle
+    English dialect that was easily understood at that time. William Cecil (1520–1598)
+    became the first Lord Burghley after serving Queen Elizabeth I. He was born at
+    a house in the centre of Bourne that is now the Burghley Arms. Dr William Dodd
+    (1729–1777), was an Anglican clergyman, man of letters and forger. He was prosecuted,
+    sentenced to death and publicly hanged at Tyburn in 1777. Charles Frederick Worth
+    (1825–1895), son of a solicitor, lived at Wake House in North Street. He moved
+    to Paris and became a renowned designer of women''s fashion and the founder of
+    haute couture. The French government awarded him the Légion d''honneur. Sir George
+    White (1840-1912), MP for North West Norfolk, a seat he held for twelve years
+    until he died in 1912. He was knighted for public service in 1907.'
+- source_sentence: What football team does the Japanese player play for
+  sentences:
+  - After the meeting, Box summons up the courage to ask Lorraine (Sue Holderness)
+    on the date. The act ends with Robert's coat getting on fire because of the cigarette,
+    with "Smoke Gets in Your Eyes" on the background.
+  - is a Japanese football player. He plays for Honda Lock.
+  - As followers on Twitter and FB probably well know I’ve been up to more than a
+    spot of preserving of late. It’s my latest addiction, as if I need any more of
+    those. My Dad’s the King of Jams, Chutneys and Pickles and I have a feeling he’s
+    passed his enthusiastic genes for it on to me!. Which is great, but time consuming.
+    Many an evening has been spent peeling, dicing, de-stoning, chopping, stirring,
+    testing, sterilising and jarring. And then obviously the tasting. And all the
+    crackers, bread and cheese to go with it!. I rarely get to bed much before midnight
+    on my chutneying nights. And to be honest my cupboards are now fit to bursting
+    with so many goodies, but at least I have christmas presents totally nailed this
+    year. My Dad’s been making Hedgerow Chutney for years, and it happens to be everyone’s
+    favourite of all his chutney recipes (and he makes quite a number!). Each autumn
+    he takes a long walk around the field at the back of his house in Herefordshire
+    picking all the freebie hedgerow goodies he can find and transforms them into
+    this marvellously fruitful chutney. There’s always plenty of damsons, bullaces,
+    sloes, blackberries and a few elderberries. Plus pears or apples for smoothing
+    and bulking out. We don’t have quite the same fruit in our hedgerows in France
+    but I thought I’d make my own French version picking the fruit from our garden
+    and nearby tracks and lanes, managing to find plenty of figs, greengages, plums,
+    pears, blackberries and sloes just before the season finished a couple of weeks
+    ago. We’ve elderberries here too but they were way past their best by the time
+    I got into full chutney mode. There’s no escaping how time consuming and labourious
+    chutney making can be, especially when using so much fruit that needs hefty preparatory
+    work. I realise now why it’s a hobby generally taken up by retired folk. But the
+    results are so worth it, if you can spare it set aside a whole evening in the
+    kitchen and wile away the hours getting lost in music or the radio or even catching
+    up on a few programmes on You Tube.
+- source_sentence: What is the purpose of Business Intelligence
+  sentences:
+  - 'College career
+
+    Proctor played as a defensive lineman for the North Carolina Central Eagles from
+    2008 to 2012. He was redshirted in 2008.'
+  - The purpose of Business Intelligence is the transformation of raw data into meaningful
+    information which can be used to make better business decisions. Business Intelligence
+    grew out of Decision Support systems and is all about collecting data from disparate
+    sources, conforming and integrating that data into central repositories which
+    support reporting and analysis activities.
+  - You have to show the police courtesy, they are only human. No one even WANTS for
+    the judicial system to work. They are too lazy.
+- source_sentence: How does the speaker feel about Battle Symphony
+  sentences:
+  - It's a symptomless prearranged fact that when you afford your babe a infant work
+    you motivate the status system, bolster the infant's stressed system, eat up colic,
+    and harden your in bondage next to your kid. Now, how satisfying is that
+  - Piquet passed Laffite to become the race's fifth different leader. Senna reached
+    second just 1.7 seconds behind Piquet by passing Laffite, who then pitted for
+    tires. With the two of them in front on their own, and Piquet leading by up to
+    3.5 seconds, Senna was content for the time being to follow his countryman. After
+    eight laps in the lead, Piquet pitted for tires. Senna regained first place and
+    then also pitted. Piquet's 18.4 second stop was even slower than teammate Mansell's
+    had been, but when he returned to the track, the two-time champion got the bit
+    between his teeth. Running second behind Senna, Piquet set the fastest lap of
+    the race on lap 41, but with a pit stop ten seconds quicker than Piquet's, Senna
+    was able to retain the lead. On the very next lap, the 42nd, Piquet pushed a bit
+    too much, and crashed hard at the left-hand corner before the last chicane. He
+    ended up in the tire barrier, unhurt, but with his car in a very precarious position.
+    The crane, present for just that reason, was unable to move the car. Arnoux, now
+    16.6 seconds behind in second, took a second a lap off Senna's lead for five laps
+    while a yellow was displayed in the corner where Piquet had crashed. As soon as
+    the yellow flag was gone, Arnoux went wide and hit Piquet's abandoned Williams!
+    The Frenchman decided that his car was not damaged, and attempted to rejoin the
+    field, but did so right in front of Thierry Boutsen's Arrows-BMW, sidelining both
+    cars. Very uncharacteristic of a street race, these three – Piquet, Arnoux and
+    Boutsen – were the only drivers all afternoon to retire due to accidents.
+  - Like Battle Symphony, it's not bad. It's just extremely boring.
+- source_sentence: When did he migrate to New South Wales
+  sentences:
+  - 'predict ministry in a sales and special floor being Job to the vulnerability
+    diver. team: This research will work last for either, often, and also obtaining
+    spreadsheets in the funny wedding power of the usability time. Physical Demands:
+    The exclusive transitions was temporarily need perfect of those that must share
+    developed by an position to badly do the animal objectives of this source. necessary
+    terabytes may pay acted to increase streets with hearts to address the professional
+    items. solely, the job will distract, Coordinate and be inbox security fun interdisciplinary
+    operations that might read in back of 20 updates The service will properly be
+    to like the detection throughout the use: logging, including, killing, teaching,
+    leading, preparing, operating, and using.'
+  - "Shizuka Shirakawa, Scholar of Chinese-language literature. Horin Fukuoji, Nihonga\
+    \ painter. 2005\n Mitsuko Mori. Actress. Makoto Saitō (1921–2008). Political scientist,\
+    \ specializing in American diplomatic and political history. Ryuzan Aoki, Ceramic\
+    \ artist. Toshio Sawada, Civil engineer. Shigeaki Hinohara, Doctor. 2006\n Yoshiaki\
+    \ Arata. A pioneer of nuclear fusion research. Jakuchō Setouchi. Writer/Buddhist\
+    \ nun. Hidekazu Yoshida. Music critic. Chusaku Oyama, Nihonga painter. Miyohei\
+    \ Shinohara, Economist. 2007\n Akira Mikazuki. Former justice minister and professor\
+    \ emeritus. Shinya Nakamura. Sculptor. Kōji Nakanishi. Organic chemist. Tokindo\
+    \ Okada, Developmental biologist. Shigeyama Sensaku, Kyogen performer. 2008\n\
+    \ Hironoshin Furuhashi (1928–2009). Sportsman and sports bureaucrat. Kiyoshi Itō.\
+    \ A mathematician whose work is now called Itō calculus. Donald Keene."
+  - He attended Derby Grammar School and Beaufort House in London, and migrated to
+    New South Wales in 1883. He settled in Newcastle, where he worked as a shipping
+    agent, eventually partnering with his brothers in a firm. On 6 May 1893 he married
+    Gertrude Mary Saddington, with whom he had five children.
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on BAAI/bge-m3
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3). 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.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision 5617a9f61b028005a4858fdac845db406aefb181 -->
+- **Maximum Sequence Length:** 1024 tokens
+- **Output Dimensionality:** 1024 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
+  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("Jrinky/model3")
+# Run inference
+sentences = [
+    'When did he migrate to New South Wales',
+    'He attended Derby Grammar School and Beaufort House in London, and migrated to New South Wales in 1883. He settled in Newcastle, where he worked as a shipping agent, eventually partnering with his brothers in a firm. On 6 May 1893 he married Gertrude Mary Saddington, with whom he had five children.',
+    'Shizuka Shirakawa, Scholar of Chinese-language literature. Horin Fukuoji, Nihonga painter. 2005\n Mitsuko Mori. Actress. Makoto Saitō (1921–2008). Political scientist, specializing in American diplomatic and political history. Ryuzan Aoki, Ceramic artist. Toshio Sawada, Civil engineer. Shigeaki Hinohara, Doctor. 2006\n Yoshiaki Arata. A pioneer of nuclear fusion research. Jakuchō Setouchi. Writer/Buddhist nun. Hidekazu Yoshida. Music critic. Chusaku Oyama, Nihonga painter. Miyohei Shinohara, Economist. 2007\n Akira Mikazuki. Former justice minister and professor emeritus. Shinya Nakamura. Sculptor. Kōji Nakanishi. Organic chemist. Tokindo Okada, Developmental biologist. Shigeyama Sensaku, Kyogen performer. 2008\n Hironoshin Furuhashi (1928–2009). Sportsman and sports bureaucrat. Kiyoshi Itō. A mathematician whose work is now called Itō calculus. Donald Keene.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 1024]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+* Size: 11,808 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                             |
+  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                               |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 17.85 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 186.46 tokens</li><li>max: 1024 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                 | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
+  |:-----------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>What type of tournament structure was used in this freestyle wrestling competition</code>                        | <code>This freestyle wrestling competition consisted of a single-elimination tournament, with a repechage used to determine the winners of two bronze medals. Results<br>Legend<br>F — Won by fall<br><br>Final<br><br>Top half<br><br>Bottom half<br><br>Repechage<br><br>References<br>Official website<br><br>Women's freestyle 58 kg<br>World</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
+  | <code>What was the status of Josip Broz Tito under the 1974 Constitution of Yugoslavia regarding his presidency</code> | <code>1 Wednesday, 22 April 1998. 2 (8.30 a.m.). 3 JUDGE CASSESE: Good morning. May I ask the<br>4 Registrar to call out the case number, please. 5 THE REGISTRAR: Case number IT-95-13a-T,<br>6 Prosecutor versus Slavko Dokmanovic. 7 MR. NIEMANN: My name is Niemann. I appear<br>8 with my colleagues, Mr. Williamson, Mr. Waespi and<br>9 Mr. Vos. 10 MR. FILA: My name is Mr. Toma Fila and<br>11 I appear with Ms. Lopicic and Mr. Petrovic in Defence of<br>12 my client, Mr. Slavko Dokmanovic. 13 JUDGE CASSESE: Mr. Dokmanovic, can you<br>14 follow me? Before we call the witness, may I ask you<br>15 whether you agree to this note from the Registrar about<br>16 the two documents which we discussed yesterday -- you<br>17 have probably received the English translation of the<br>18 bibliography of our witness, plus the missing pages of<br>19 the other document, so I think it is agreed that they<br>20 can be admitted into evidence. 21 MR. NIEMANN: Yes. 22 JUDGE CASSESE: Shall we proceed with the<br>24 MR. FILA: Your Honour, before we continue<br>25 wi...</code> |
+  | <code>How quickly can you get loan approval and funds transferred with Crawfort</code>                                 | <code>Then click on the submit button, and it’s done. Make your dream come true with Crawfort<br>When you all submit the loan form, then the agency takes a few hours to process and for approval of the loan. Not only that, you can get your loan amount in your account within a day after getting approval. Many money lenders all take more time in processing things and to credit the amount as well. So, for all that, a customer suffers more as they can’t get the money immediately. But here all these things are not done, and the staff here always make sure to provide you best and fast services. For all these things, you can get the best loan services from here without any doubt.</code>                                                                                                                                                                                                                                                                                                                                                                                       |
+* Loss: <code>selfloss.Infonce</code> with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+* Size: 1,476 evaluation samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                             |
+  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                               |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 17.61 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 171.81 tokens</li><li>max: 1024 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                            | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
+  |:--------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>What is Hector Guimard best known for</code>                                                | <code>Hector Guimard (, 10 March 1867 – 20 May 1942) was a French architect and designer, and a prominent figure of the Art Nouveau style. He achieved early fame with his design for the Castel Beranger, the first Art Nouveau apartment building in Paris, which was selected in an 1899 competition as one of the best new building facades in the city. He is best known for the glass and iron edicules or canopies, with ornamental Art Nouveau curves, which he designed to cover the entrances of the first stations of the Paris Metro. Between 1890 and 1930, Guimard designed and built some fifty buildings, in addition to one hundred and forty-one subway entrances for Paris Metro, as well as numerous pieces of furniture and other decorative works. However, in the 1910s Art Nouveau went out of fashion and by the 1960s most of his works had been demolished, and only two of his original Metro edicules were still in place. Guimard's critical reputation revived in the 1960s, in part due to subsequent acquisit...</code> |
+  | <code>What does Mark Kantrowitz say about the inclusion of loans in financial aid packages</code> | <code>"They don't always understand that part of the financial aid package includes loans," he says. But loans "don't really reduce your costs," explains Mark Kantrowitz, founder of the financial aid website FinAid.org and publisher of Edvisors Network. "They simply spread them out over time. ... A loan is a loan.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
+  | <code>How can Ayurveda support women's health during menopause</code>                             | <code>Especially as we journey towards menopause, Ayurveda is there to support us with its millenary wisdom. These are some easy routines to incorporate for the daily care of the vulva and vagina, our most delicate flower. Sesame oil: our best allied against dryness, it cannot be missing in our diet.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
+* Loss: <code>selfloss.Infonce</code> with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 2
+- `per_device_eval_batch_size`: 2
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 5
+- `warmup_ratio`: 0.1
+- `fp16`: True
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 2
+- `per_device_eval_batch_size`: 2
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 5
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: 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
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.2033 | 100  | 0.2694        | 0.0690          |
+| 0.4065 | 200  | 0.0822        | 0.0528          |
+| 0.6098 | 300  | 0.0689        | 0.0497          |
+| 0.8130 | 400  | 0.0644        | 0.0469          |
+| 1.0163 | 500  | 0.0643        | 0.0443          |
+| 1.2195 | 600  | 0.0378        | 0.0473          |
+| 1.4228 | 700  | 0.04          | 0.0479          |
+| 1.6260 | 800  | 0.0358        | 0.0461          |
+| 1.8293 | 900  | 0.0332        | 0.0507          |
+| 2.0325 | 1000 | 0.0283        | 0.0538          |
+
+
+### Framework Versions
+- Python: 3.12.3
+- Sentence Transformers: 3.4.0
+- Transformers: 4.42.4
+- PyTorch: 2.2.0+cu121
+- Accelerate: 1.3.0
+- Datasets: 3.2.0
+- Tokenizers: 0.19.1
+
+## 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",
+}
+```
+
+#### Infonce
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

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+{
+  "_name_or_path": "/root/autodl-tmp/work_dir/models/model3/checkpoint-1000",
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 8194,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.42.4",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.4.0",
+    "transformers": "4.42.4",
+    "pytorch": "2.2.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+size 2271064456

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
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+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
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+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 1024,
+  "do_lower_case": false
+}

special_tokens_map.json

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+  "bos_token": {
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+    "rstrip": false,
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+}

tokenizer.json

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+size 17083053

tokenizer_config.json

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+  "padding_side": "right",
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+  "sp_model_kwargs": {},
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+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}