Upload openvino_tokenizer.xml with huggingface_hub
Browse files- openvino_tokenizer.xml +902 -0
openvino_tokenizer.xml
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|
| 384 |
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<dim>-1</dim>
|
| 385 |
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</port>
|
| 386 |
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|
| 387 |
+
<dim>-1</dim>
|
| 388 |
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</port>
|
| 389 |
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</output>
|
| 390 |
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</layer>
|
| 391 |
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|
| 392 |
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|
| 393 |
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| 394 |
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| 395 |
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|
| 396 |
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</port>
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| 397 |
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|
| 398 |
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</layer>
|
| 399 |
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<layer id="32" name="StringTensorUnpack_306487" type="StringTensorUnpack" version="extension">
|
| 400 |
+
<data mode="begins_ends" />
|
| 401 |
+
<input>
|
| 402 |
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<port id="0" precision="U8">
|
| 403 |
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<dim>14600</dim>
|
| 404 |
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</port>
|
| 405 |
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| 406 |
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|
| 407 |
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|
| 408 |
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|
| 409 |
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| 410 |
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|
| 411 |
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|
| 412 |
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| 413 |
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|
| 414 |
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|
| 415 |
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</port>
|
| 416 |
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| 417 |
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|
| 418 |
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|
| 419 |
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| 420 |
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| 422 |
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|
| 423 |
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</port>
|
| 424 |
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</output>
|
| 425 |
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</layer>
|
| 426 |
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<layer id="34" name="BPETokenizer_306495" type="BPETokenizer" version="extension">
|
| 427 |
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<data unk_token="" fuse_unk="false" suffix_indicator="" end_suffix="" byte_fallback="false" cache_capacity="30268" />
|
| 428 |
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|
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| 437 |
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| 439 |
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| 440 |
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| 441 |
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| 443 |
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| 445 |
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| 446 |
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| 447 |
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| 448 |
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|
| 449 |
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|
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| 452 |
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|
| 455 |
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| 456 |
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|
| 457 |
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|
| 458 |
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| 459 |
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|
| 460 |
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|
| 461 |
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|
| 463 |
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|
| 464 |
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| 465 |
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|
| 466 |
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|
| 467 |
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|
| 468 |
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|
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|
| 470 |
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|
| 473 |
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| 475 |
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|
| 476 |
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|
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|
| 479 |
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| 480 |
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|
| 481 |
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| 482 |
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|
| 483 |
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| 484 |
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|
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|
| 486 |
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|
| 487 |
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| 488 |
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|
| 489 |
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|
| 490 |
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| 491 |
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|
| 492 |
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|
| 493 |
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</port>
|
| 494 |
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|
| 495 |
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</layer>
|
| 496 |
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|
| 497 |
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<data auto_broadcast="numpy" />
|
| 498 |
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|
| 499 |
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|
| 500 |
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| 501 |
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|
| 502 |
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| 503 |
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|
| 504 |
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| 505 |
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| 506 |
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| 508 |
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| 509 |
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| 510 |
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| 511 |
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| 512 |
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|
| 513 |
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|
| 514 |
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|
| 515 |
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|
| 516 |
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| 517 |
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| 518 |
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|
| 519 |
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| 520 |
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|
| 521 |
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| 522 |
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| 523 |
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| 524 |
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| 525 |
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| 526 |
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|
| 527 |
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|
| 528 |
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|
| 529 |
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| 530 |
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| 531 |
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|
| 532 |
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|
| 533 |
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|
| 534 |
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| 535 |
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| 536 |
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| 537 |
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| 538 |
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| 539 |
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|
| 540 |
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| 541 |
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| 542 |
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|
| 543 |
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|
| 544 |
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|
| 545 |
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| 546 |
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| 547 |
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|
| 548 |
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|
| 549 |
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| 550 |
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<output>
|
| 551 |
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<port id="0" precision="I32">
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| 552 |
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<dim>3</dim>
|
| 553 |
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| 554 |
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</output>
|
| 555 |
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</layer>
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| 556 |
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|
| 557 |
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|
| 558 |
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| 561 |
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|
| 562 |
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| 563 |
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|
| 564 |
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|
| 565 |
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|
| 566 |
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|
| 567 |
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| 568 |
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|
| 569 |
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<dim>-1</dim>
|
| 570 |
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| 571 |
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|
| 572 |
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|
| 573 |
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| 574 |
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|
| 575 |
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|
| 576 |
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|
| 577 |
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|
| 578 |
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<dim>3</dim>
|
| 579 |
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| 580 |
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|
| 581 |
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|
| 582 |
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|
| 583 |
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|
| 584 |
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| 585 |
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| 586 |
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| 587 |
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| 588 |
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|
| 589 |
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|
| 590 |
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| 591 |
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|
| 592 |
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|
| 593 |
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| 594 |
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|
| 595 |
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|
| 596 |
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| 597 |
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| 598 |
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|
| 599 |
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| 600 |
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|
| 601 |
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|
| 602 |
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|
| 603 |
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| 604 |
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|
| 605 |
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| 606 |
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| 607 |
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| 608 |
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| 609 |
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|
| 610 |
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| 611 |
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|
| 612 |
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|
| 613 |
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|
| 614 |
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<dim>-1</dim>
|
| 615 |
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|
| 616 |
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|
| 617 |
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|
| 618 |
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|
| 619 |
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| 620 |
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<output>
|
| 621 |
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<port id="0" precision="I32" />
|
| 622 |
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| 623 |
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</layer>
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| 624 |
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|
| 625 |
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|
| 626 |
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|
| 627 |
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|
| 628 |
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<dim>-1</dim>
|
| 629 |
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|
| 630 |
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|
| 631 |
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|
| 632 |
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<output>
|
| 633 |
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|
| 634 |
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| 635 |
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| 636 |
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|
| 637 |
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|
| 638 |
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<output>
|
| 639 |
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| 640 |
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| 641 |
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|
| 642 |
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|
| 643 |
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| 644 |
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|
| 645 |
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| 646 |
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<dim>-1</dim>
|
| 647 |
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| 648 |
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| 649 |
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| 650 |
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| 651 |
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| 652 |
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| 653 |
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| 654 |
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|
| 655 |
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| 656 |
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| 657 |
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| 658 |
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| 659 |
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| 660 |
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<dim>-1</dim>
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| 661 |
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| 662 |
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| 663 |
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| 664 |
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<dim>-1</dim>
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| 665 |
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| 666 |
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| 667 |
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| 668 |
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| 669 |
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| 670 |
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| 671 |
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| 672 |
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| 673 |
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<dim>-1</dim>
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| 674 |
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| 675 |
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| 676 |
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| 677 |
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| 678 |
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|
| 679 |
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<dim>-1</dim>
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| 680 |
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| 681 |
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|
| 682 |
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| 683 |
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|
| 684 |
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|
| 685 |
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|
| 686 |
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| 687 |
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|
| 688 |
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<dim>-1</dim>
|
| 689 |
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|
| 690 |
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| 691 |
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<output>
|
| 692 |
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|
| 693 |
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|
| 694 |
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<dim>-1</dim>
|
| 695 |
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| 696 |
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| 697 |
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| 698 |
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|
| 699 |
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|
| 700 |
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|
| 701 |
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| 702 |
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| 703 |
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| 704 |
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|
| 705 |
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| 706 |
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|
| 707 |
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| 708 |
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<dim>-1</dim>
|
| 709 |
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| 710 |
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| 711 |
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| 712 |
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| 713 |
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| 714 |
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| 715 |
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| 716 |
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| 717 |
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| 718 |
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| 719 |
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| 720 |
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| 721 |
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|
| 722 |
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<dim>-1</dim>
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| 723 |
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| 724 |
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| 725 |
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|
| 726 |
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<dim>-1</dim>
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| 727 |
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| 728 |
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| 729 |
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| 730 |
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| 731 |
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| 732 |
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|
| 733 |
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| 734 |
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|
| 735 |
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<dim>-1</dim>
|
| 736 |
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| 737 |
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|
| 738 |
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|
| 739 |
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|
| 740 |
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<dim>-1</dim>
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| 741 |
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<dim>-1</dim>
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| 742 |
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| 743 |
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| 744 |
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| 745 |
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| 746 |
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| 747 |
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| 748 |
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| 749 |
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| 750 |
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<dim>-1</dim>
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| 751 |
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| 752 |
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| 753 |
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| 754 |
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| 755 |
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| 756 |
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<dim>-1</dim>
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| 757 |
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| 758 |
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| 759 |
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| 760 |
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| 761 |
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| 762 |
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| 763 |
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| 764 |
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<dim>-1</dim>
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| 765 |
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| 766 |
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| 767 |
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| 768 |
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<chat_template value="[gMASK]<sop>{% for item in messages %}{% if item['tools'] is defined %}<|system|> 你是一个名为 ChatGLM 的人工智能助手。你是基于智谱AI训练的语言模型 GLM-4 模型开发的，你的任务是针对用户的问题和要求提供适当的答复和支持。 # 可用工具{% set tools = item['tools'] %}{% for tool in tools %}{% if tool['type'] == 'function' %} ## {{ tool['function']['name'] }} {{ tool['function'] | tojson(indent=4) }} 在调用上述函数时，请使用 Json 格式表示调用的参数。{% elif tool['type'] == 'python' %} ## python 当你向 `python` 发送包含 Python 代码的消息时，该代码将会在一个有状态的 Jupyter notebook 环境中执行。 `python` 返回代码执行的输出，或在执行 60 秒后返回超时。 `/mnt/data` 将会持久化存储你的文件。在此会话中，`python` 无法访问互联网。不要使用 `python` 进行任何网络请求或者在线 API 调用，这些在线内容的访问将不会成功。{% elif tool['type'] == 'simple_browser' %} ## simple_browser 你可以使用 `simple_browser` 工具。该工具支持以下函数： `search(query: str, recency_days: int)`：使用搜索引擎进行查询并显示结果，可以使用 `recency_days` 参数控制搜索内容的时效性。 `mclick(ids: list[int])`：获取一系列指定 id 的页面内容。每次调用时，须选择3-10个页面。选择多个角度的页面，同时尽可能选择可信任的信息来源。考虑到部分页面是无法加载的，你也可以多打开一些可能有用的页面而不用担心内容过多。 `open_url(url: str)`：打开指定的 URL。 使用 `【{引用 id}†{引用文本}】` 来引用内容。 操作步骤：1. 使用 `search` 来获得信息列表; 2. 使用 `mclick` 来获取指定 ID 页面的内容; 3. 根据获得的内容进行回复。在回复中应当引用信息来源。 如果用户提供了 URL，也可以用 `open_url` 直接打开页面。 如果初次搜索结果没有找到合适的信息，也可以再次使用 `search` 进行搜索。{% elif tool['type'] == 'cogview' %} ## cogview 如果用户的请求中包含了对图像的描述，你可以使用 `cogview` 来生成图像并展示给用户。你需要向 `cogview` 发送图像描述，规则： - 发送给 `cogview` 的消息必须使用英语。用户的中文描述必须完全翻译为英语。 - 应当尽可能详细地描述图像生成的需求，需求描述约 100 英文单词。 - 保持用户原始描述的意图。不要虚构内容或者没见过的人物。 - 如无特殊说明，所在地为中国，持有中国立场并遵循中国社会主义价值观。{% endif %}{% endfor %}{% endif %}{% if item['content'] %}<|{{ item['role'] }}|>{{ item['metadata'] }} {{ item['content'] }}{% endif %}{% endfor %}{% if add_generation_prompt %}<|assistant|>{% endif %}" />
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