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--- |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:11347 |
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- loss:MultipleNegativesRankingLoss |
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base_model: vinai/phobert-base |
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widget: |
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- source_sentence: "Beefsteak 123 la mot dia chi ban banh mi chao, beefsteak cuc ngon\ |
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\ tai Can Tho ma ban nen mot gan ghe den. Khong gian quan rong rai, sach se, phuc\ |
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\ vu nhanh nhen, gia ca hop ly. Banh mi chao duong Nguyen Van Troi noi tieng ban\ |
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\ banh mi thom ngon, chat luong. Banh mi tai day chia ra lam 2 phan: co thit bo\ |
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\ ma khong thit bo.\n\nQuan Beefsteak 123 la mot dia diem ly tuong cho nhung nguoi\ |
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\ yeu thich thit bo va cac mon an ngon khac. Quan noi tieng voi su ket hop tuyet\ |
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\ voi giua thit bo, pate va trung op la. Neu ban muon thu nhung mon khac, quan\ |
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\ cung co san xuc xich, ca moi, cha lua va xiu mai. Menu cua quan duoc chia thanh\ |
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\ tung phan da duoc ket hop san de ban de dang lua chon. Vi du nhu bo op la pate\ |
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\ xuc xich hoac bo op la pate cha lua. Ban cung co the tao ra cac to hop rieng\ |
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\ cua rieng minh nhu op la ca moi xiu mai.Mot dieu dac biet khi den quan la khi\ |
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\ ban goi mot phan, ban se duoc tang mien phi mot dia xa lach tron. Day la cach\ |
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\ hoan hao de ket hop khau vi cua ban voi cac loai rau song tuoi ngon.Voi khong\ |
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\ gian thoai mai va phuc vu nhanh chong, quan Beefsteak 123 mang den cho ban trai\ |
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\ nghiem am thuc doc dao va ngon mieng. Hay ghe tham quan de thuong thuc nhung\ |
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\ mon an tuyet voi nay!\n\nTHONG TIN LIEN HE:\nDia chi: 9B Nguyen Van Troi, Phuong\ |
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\ Xuan Khanh, Can Tho\nDien thoai: 0907 713 458\nGio mo cua: 06:00 - 14:00\nGia\ |
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\ tham khao: 20.000d - 40.000d\nFanpage: https://www.facebook.com/Beefsteak-123-143170999350605/\n\ |
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\n Goi dien" |
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sentences: |
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- Beefsteak 123 - Nguyen Van Troi |
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- Pho Ngon 37 |
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- Khong tra no hay chi tien ngay Tet |
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- source_sentence: 'KCC - Pho & Com Ga Xoi Mam la quan an duoc nhieu nguoi yeu thich |
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tai so 6 Ton That Thuyet, Nam Tu Liem, Ha Noi. Noi day voi khong gian am cung, |
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rat thich hop cho nhung bua an ben ban be, dong nghiep. Day la quan duoc nhieu |
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thuc khach danh gia cao ca dich vu lan chat luong do an. Den voi KCC - Pho & Com |
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Ga Xoi Mam ngoai pho la mon duoc yeu thich nhat ra, quan con co vo so cac mon |
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an hap dan nhu: com rang dui ga xoi mam, com rang dua bo, com rang cai bo, pho |
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xao bo, com nong dui ga xoi mam, mi xao bo, com nong cai bo, com nong dua bo. |
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Doc va la tu nhung hat com gion rum, cung voi do la huong vi cua nuoc sot dac |
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trung va bi truyen ngam sau vao tan ben trong. |
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Cac mon nay tuy binh di trong cach che bien nhung mang lai huong vi am thuc manh |
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me, du de lam to mo bat cu thuc khach nao khi thuong thuc. KCC - Pho & Com Ga |
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Xoi Mam cam ket mang den cho nguoi tieu dung nhung san pham ngon an toan, co loi |
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cho suc khoe voi gia rat hop ly. Ban dang o Ton That Thuyet, Ha Noi va dang ban |
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khoan khong biet dia chi an pho nao ngon thi hay ghe ngay quan an KCC nhe! |
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THONG TIN LIEN HE: Dia chi: 6 Ton That Thuyet, Nam Tu Liem, Ha Noi Gio mo cua: 06:00 |
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- 14:00 | 17:30 - 22:00 |
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Dat mua ngay' |
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sentences: |
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- Nem Nuong Hai Anh |
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- Ca basa kho thom |
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- KCC - Pho & Com Ga Xoi Mam |
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- source_sentence: Banh canh ca loc duoc lam tu bot gao va ca loc. Bot gao sau khi |
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duoc can mong thanh soi vua an thi duoc tha vao noi nuoc luoc Ca loc go lay phan |
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thit, uop chut gia vi cho dam vi. Phan xuong ca khong bi bo di ma duoc giu lai |
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gia nhuyen, loc lay phan nuoc ca roi do vao phan nuoc dung. Mon banh canh ca loc |
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ngon nhat la khi an con nong, vua chan vua hup vua xuyt xoa cai vi cay nong. Neu |
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an trong ngay dong thi qua tuyet voi roi phai khong nao. Mot to banh canh ca loc |
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chi co gia khoang 30.000 dong thoi cac ban nhe. |
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sentences: |
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- Banh canh ca loc |
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- Bun oc, bun oc chan |
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- Nha hang Trung Duong Marina |
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- source_sentence: 'Nguyen lieu:Bap chuoi 1 cai Chanh 1 trai Bot chien gion 75 gr |
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Dau an 100 ml Nuoc mam 3 muong canh Bot ngot 1 muong ca phe Tuong ot 1 muong canh |
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Duong 1 muong canh Ot bot 1 muong ca pheCach che bien:So che bap chuoi: Dung tay |
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tach bap chuoi thanh nhung cong nho, sau do ngam bap chuoi vao trong thau nuoc |
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chanh pha loang de giup bap chuoi khong bi tham den. Tiep tuc go bo nhuy trong |
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bap chuoi roi rua sach lai voi nuoc.Nhung bot va chien bap chuoi: Bap chuoi sau |
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khi tach roi va rua sach ban cho bap chuoi ra to, do vao 75gr bot chien gion, |
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dao deu cho bot tham vao bap chuoi. Bac chao len bep cung voi 100ml dau an dun |
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soi (luong dau ngap bap chuoi), sau do cho bap chuoi da ao bot vao chien tren |
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lua vua khoang 5 - 10 phut cho bap chuoi chin vang deu thi vot ra de rao dau.Lam |
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bap chuoi chien nuoc mam: Bac mot cai chao khac cho vao 10ml dau an (tan dung |
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luong dau con du khi chien bap chuoi), roi cho vao 3 muong canh nuoc mam, 1 muong |
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ca phe bot ngot, 1 muong canh tuong ot, 1 muong canh duong, 1 muong ca phe ot |
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bot khuay tan hon hop cho sanh vang lai khoang 3 phut tren lua vua. Cuoi cung |
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ban cho bap chuoi da chien vang vao dao deu them 3 phut roi tat bep.Thanh pham: |
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Bap chuoi gion rum hoa quyen voi vi man man ngot ngot cua nuoc mam, an kem com |
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trang se cuc ki ngon mieng day. Mon an vo cung de lam nay se khien gia dinh ban |
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tam tac khen ngon.' |
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sentences: |
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- Nha Hang Ca Hoi Song Nhi |
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- Com nhoi thit hap ot chuong |
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- Hoa chuoi chien nuoc mam |
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- source_sentence: "Noi tieng ve do lau doi va huong vi mon an nay o Ha Noi thi phai\ |
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\ ke den hang Banh Duc Nong Thanh Tung. Banh o day hap dan o do deo dai cua bot,\ |
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\ thit nam du day va nem nem vua mieng. Khi phuc vu, mon an nong sot toa ra mui\ |
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\ huong thom lung tu bot, hanh phi, nuoc mam. Mon banh duc o day duoc chan ngap\ |
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\ nuoc mam pha loang vi ngot, hoi man man, co thit bam voi nam meo va rat nhieu\ |
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\ hanh kho da phi vang.Mon banh duc o Banh Duc Nong Thanh Tung duoc chan ngap\ |
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\ nuoc mam pha loang vi ngot, hoi man man, co thit bam voi nam meo va rat nhieu\ |
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\ hanh kho da phi vang. Cach an nay hoi giong voi mon banh gio chan nuoc mam thit\ |
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\ bam o quan pho chua Lang Son gan cho Ban Co. La mon qua an nhe nhang, vua du\ |
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\ lung lung bung, co ve dan da nen rat nhieu nguoi them them, nho nho. Banh duc\ |
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\ nong Ha Noi o day khong bi pha them bot dau xanh nen van giu nguyen duoc huong\ |
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\ vi dac trung. Dac biet, phan nhan con duoc tron them mot it cu dau xao tren\ |
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\ ngon lua lon nen giu duoc do ngot gion.THONG TIN LIEN HE:Dia chi: 112 Truong\ |
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\ Dinh, Quan Hai Ba Trung, Ha NoiGio mo cua: 10:00 - 21:00Dia diem chat luong:\ |
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\ 4.7/5 (14 danh gia tren Google)\n Chi duong Danh gia Google" |
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sentences: |
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- Banh Duc |
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- Let's Eat Buffet |
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- Banh bi do |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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--- |
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|
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# SentenceTransformer based on vinai/phobert-base |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [vinai/phobert-base](https://huggingface.co/vinai/phobert-base). It maps sentences & paragraphs to a 768-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:** [vinai/phobert-base](https://huggingface.co/vinai/phobert-base) <!-- at revision c1e37c5c86f918761049cef6fa216b4779d0d01d --> |
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- **Maximum Sequence Length:** 128 tokens |
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- **Output Dimensionality:** 768 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': 128, 'do_lower_case': False}) with Transformer model: RobertaModel |
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(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': 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("trongvox/Phobert-Sentence") |
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# Run inference |
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sentences = [ |
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'Noi tieng ve do lau doi va huong vi mon an nay o Ha Noi thi phai ke den hang Banh Duc Nong Thanh Tung. Banh o day hap dan o do deo dai cua bot, thit nam du day va nem nem vua mieng. Khi phuc vu, mon an nong sot toa ra mui huong thom lung tu bot, hanh phi, nuoc mam. Mon banh duc o day duoc chan ngap nuoc mam pha loang vi ngot, hoi man man, co thit bam voi nam meo va rat nhieu hanh kho da phi vang.Mon banh duc o Banh Duc Nong Thanh Tung duoc chan ngap nuoc mam pha loang vi ngot, hoi man man, co thit bam voi nam meo va rat nhieu hanh kho da phi vang. Cach an nay hoi giong voi mon banh gio chan nuoc mam thit bam o quan pho chua Lang Son gan cho Ban Co. La mon qua an nhe nhang, vua du lung lung bung, co ve dan da nen rat nhieu nguoi them them, nho nho. Banh duc nong Ha Noi o day khong bi pha them bot dau xanh nen van giu nguyen duoc huong vi dac trung. Dac biet, phan nhan con duoc tron them mot it cu dau xao tren ngon lua lon nen giu duoc do ngot gion.THONG TIN LIEN HE:Dia chi: 112 Truong Dinh, Quan Hai Ba Trung, Ha NoiGio mo cua: 10:00 - 21:00Dia diem chat luong: 4.7/5 (14 danh gia tren Google)\n Chi duong Danh gia Google', |
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'Banh Duc', |
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'Banh bi do', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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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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<!-- |
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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: 11,347 training samples |
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* Columns: <code>sentence_0</code> and <code>sentence_1</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence_0 | sentence_1 | |
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|:--------|:-------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 73 tokens</li><li>mean: 127.74 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 8.16 tokens</li><li>max: 24 tokens</li></ul> | |
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* Samples: |
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| sentence_0 | sentence_1 | |
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|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------| |
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| <code>Mamadeli la mot dia chi giup ban giai quyet con them com ga, mi y chuan vi nhat. Nhan vien tai quan nay kha de chiu va chieu khach. Mot suat com ga ta bao gom mot phan com mem, thit ga ta xe thom phuc va dia nuoc mam gung chan voi sot trung rat dam da.Giua long Sai Gon hoa le lai co huong vi cua mon com ga nuc tieng thi con dieu gi khien ban ban khoan ma khong thuong thuc nhi. Thuc don phong phu, gia ca phai chang voi huong vi mon an hoan hao dung vi hap dan la li do giup quan thu hut duoc dong dao khach hang ghe toi thuong xuyen.<br><br>Ngoai ra, voi cach trinh bay mon an day bat mat va mau sac chac chan cac thuc khach khi den day se khong the roi mat khoi mon an dau. Team thich song ao tung chao nghe toi day chac hao huc lam vi do an vua ngon, vua co hinh de song ao chat luong.Va khien ai cung thom them ghen ti khi ban co co hoi duoc thu va trai nghiem o Mamadeli do. Neu ban muon tan huong tai nha thi hay yen tam, Mamadeli hien tai da co mat tren cac app giao hang, cac ban co the theo doi...</code> | <code>Mamadeli - Com ga & Mi y</code> | |
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| <code>Nguyen lieu:Thit heo xay 300 gr Toi bam 2 muong ca phe Hanh tim bam 2 muong ca phe Gung bam 1 muong ca phe Nuoc mam 1/2 muong canh Nuoc tuong 1 muong canh Bot nang 2 muong canh Giam an 2 muong canh Tuong ca 3 muong canh Dau an 2 muong canh Duong 4 muong canh Muoi 1/4 muong canhCach che bien Thit vien kho chua ngotUop thitBan uop thit voi 2 muong ca phe toi bam, 2 muong ca phe hanh tim, 1 muong ca phe gung bam, 1/4 muong ca phe muoi, 1/2 muong canh nuoc mam, 1 muong canh nuoc tuong, 2 muong canh bot nang.Sau do, ban tron deu de cac gia vi ngam vao nhau va uop khoang 15 phut.<br>Vo vien va chien thitBan vo thit thanh tung vien vua an.Ban dun nong 2 muong canh dau an o lua vua. Khi dau soi, ban cho thit vao va chien vang deu 2 mat.<br>Kho thitBan cho vao chao 4 muong canh duong, 2 muong canh giam an, 3 muong canh tuong ca va 4 muong canh nuoc loc roi dao deu.Ban rim phan nuoc sot voi thit vien 15 phut sau do tat bep va cho ra dia.<br>Thanh phamThit vien mem, thom, vua an cung voi nuoc sot chua chu...</code> | <code>Thit vien kho chua ngot</code> | |
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| <code>Nguyen lieu:1kg oc1 cu gungHanh khoToi, otSa teNuoc mam, bot ngot, duong...Cach lam:Oc giac khi mua ve, ban cung dem rua sach, roi ngam voi nuoc vo gao co cat them voilat ot trong 3 tieng de oc nhanh nha chat ban ra.Gung ban dem cao vo rua sach, bam nho.Hanh kho, toi boc sach vo. Hanh kho ban thai lat mong, con toi thi bam nhuyen.Ot tuoi rua sach, thai lat.Sau khi ngam xong, ban dem oc giac luoc voi nuoc co cho them vai lat gung hoac sa dap dap. Khi oc chin, ban lay thit oc ra cat lat va de ra dia. Dat chao len bep, cho dau an vao, khi dau soi ban cho hanh kho va toi vao phi thom. Tiep den, ban cho vao 3 muong sa te, ot cat lat, dao deu tay. Dao khoang 5 phut, ban cho oc vao deu roi nem nem voi nuoc mam, duong, bot ngot sao cho vua khau vi. Xao oc khoang 10 phut nua thi tat bep.Vay la hoan thanh mon an roi, gio day ban chi can cho mon an ra dia va cho them vai soi rau ram len tren la xong!</code> | <code>Oc giac xao sa te</code> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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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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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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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`: no |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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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`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: None |
|
- `hub_always_push`: False |
|
- `gradient_checkpointing`: False |
|
- `gradient_checkpointing_kwargs`: None |
|
- `include_inputs_for_metrics`: False |
|
- `include_for_metrics`: [] |
|
- `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 |
|
- `use_liger_kernel`: False |
|
- `eval_use_gather_object`: False |
|
- `average_tokens_across_devices`: False |
|
- `prompts`: None |
|
- `batch_sampler`: batch_sampler |
|
- `multi_dataset_batch_sampler`: round_robin |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | Training Loss | |
|
|:------:|:----:|:-------------:| |
|
| 0.7042 | 500 | 0.9125 | |
|
| 1.4085 | 1000 | 0.2277 | |
|
| 2.1127 | 1500 | 0.1527 | |
|
| 2.8169 | 2000 | 0.1009 | |
|
|
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.3.1 |
|
- Transformers: 4.47.1 |
|
- PyTorch: 2.5.1+cu121 |
|
- Accelerate: 1.2.1 |
|
- Datasets: 3.2.0 |
|
- 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", |
|
} |
|
``` |
|
|
|
#### MultipleNegativesRankingLoss |
|
```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} |
|
} |
|
``` |
|
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