SentenceTransformer based on hkunlp/instructor-xl

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: hkunlp/instructor-xl
• Maximum Sequence Length: 128 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False, 'architecture': 'T5EncoderModel'})
  (1): Pooling({'word_embedding_dimension': 768, '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': False})
  (2): Dense({'in_features': 1024, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
  (3): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

Training Details

Training Dataset

Unnamed Dataset

• Size: 16,272 training samples
• Columns: sentence_0 and sentence_1
• Approximate statistics based on the first 1000 samples:

  	sentence_0	sentence_1
  type	string	string
  details	min: 7 tokens mean: 98.93 tokens max: 128 tokens	min: 16 tokens mean: 123.26 tokens max: 128 tokens

• Samples:

  sentence_0	sentence_1
  Represent the plot: In 1943, near the village of Sobibor in the General Government of Poland, a train carrying Jewish deportees stopped at the entrance to the Sobibor extermination camp. This camp was one of the components of Operation Reinhard. The deportees were relieved of their belongings. Those who declared they had a profession deemed useful were separated from the others. A group of prisoners organized an uprising and a large-scale escape. Four hundred people escaped from the camp. One hundred people perished during the escape. The local population killed or handed over approximately 150 people to the Germans. As a testament to the failure of the Third Reich, the Sobibor camp was dismantled under orders from the German High Command. The camp commandant, SS-Oberscharführer Karl August Frenzel, was sentenced to life imprisonment. He died in 1943. Alexander Pechersky crossed the front lines and fought until the end of the war. He died at the age of 80. His heroic act was never reco...	Represent the plot: The film is based on the Sobibor revolt which occurred in 1943 in German-occupied Poland. The main character of the movie is the Jewish-Soviet soldier Alexander Pechersky, who was a lieutenant in the Red Army. In October 1943, he was deported to the Sobibor death camp, where Jews were being exterminated in gas chambers. In just three weeks, Pechersky planned an uprising with prisoners from Poland and other locations around Western Europe. This uprising was partly successful, allowing roughly 300 prisoners to escape, of whom roughly 60 survived the war.
  Represent the plot: Eric Vincent, in his thirties, leads an uneventful life with his wife, Audrey. While the couple is discussing having a child, Eric is contacted by a friend of his biological father, whom he has never met. Eric learns that his father is dead and that he can come and collect his ashes if he wishes. Initially reluctant, he eventually agrees. This sets off a chain of events that will unfold, a series of intertwined stories and destinies...	Represent the plot: Eric Vincent is in his thirties and lives an uneventful life with his wife Audrey. They are talking about having a child. Eric is contacted by a friend of his biological father he never knew about the latter's death. Eric can come and collect his father's ashes. He is reluctant but then accepts.
  Represent the plot: Marla Brenner (Anya Taylor-Joy) is an 18-year-old girl who lives with her younger brother Charlie (Gabriel Bateman) in a comfortable home in Montreal. Like Charlie, she loves Playmobil toys, and they are always inventing new stories. They especially enjoy playing knights and Vikings battling Roman soldiers. One sunny day, she is overjoyed because she has received her passport and ID card. She dreams of filling it out and exploring the world. She promises Charlie she will take him with her. But moments later, two police officers ring the Brenners' doorbell. Marla opens the door, and they announce that her parents have just been in a car accident. They did not survive… Four years later, Marla has given up on her dreams and is doing her best to take care of the house and her brother Charlie, who is now 10 years old. He hasn't lost his childlike spirit, but no longer recognizes his older sister who has "grown up" much too quickly.	Represent the plot: Marla is a formerly free-spirited girl who has grown up to be responsible yet overprotective in order to care for her brother Charlie, who has grown lonely and disconnected from her after the death of their parents in a car accident. One night, Charlie sneaks out to visit a toy museum with a Playmobil exhibit. After Marla arrives and tells Charlie off for running away, a lighthouse illuminates them and transports them to the Playmobil world. Marla and Charlie –who has been transformed into a Viking warrior– find themselves in the middle of a viking battle, and Charlie helps them until he is kidnapped by a group of pirates. Frantic to find her brother, Marla goes to the nearest town hoping to ask for help, and runs into Del, the driver of a food truck whose client refuses to pay him over pink hay that causes the town's horses to sprout wings. As Marla tries to form a posse to find Charlie, Del gets her out of trouble when she shows Viking gold to the whole town, and...

• Loss: MultipleNegativesRankingLoss with these parameters:
  json

  1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false 5}

Training Hyperparameters

Non-Default Hyperparameters

• multi_dataset_batch_sampler: round_robin

All Hyperparameters

Training Logs

Framework Versions

• Python: 3.9.21
• Sentence Transformers: 5.1.2
• Transformers: 4.57.6
• PyTorch: 2.8.0+cu128
• Accelerate: 1.10.1
• Datasets: 4.5.0
• Tokenizers: 0.22.2

Citation

BibTeX

Sentence Transformers

MultipleNegativesRankingLoss