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f7b715f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 | """Self-contained model class for binomial-marks-1.
Distributed alongside the weights on HuggingFace Hub so anyone can do:
from transformers import AutoTokenizer, AutoModel
tok = AutoTokenizer.from_pretrained("BinomialTechnologies/binomial-marks-1")
model = AutoModel.from_pretrained("BinomialTechnologies/binomial-marks-1",
trust_remote_code=True)
This file imports only from `transformers` + `torch` — no project-internal
dependencies.
Architecture:
ModernBERT-large encoder (with optional YaRN RoPE extension to 16k)
↓ (CLS + masked mean pool concatenated)
↓ (3 × MLP heads)
23 outputs:
10 × topic_mentioned (binary classification, sigmoid → BCE loss)
10 × topic_score (regression in [-2, +2] after clamp at inference)
3 × tone_score (regression in [1, 5] after clamp at inference)
"""
from __future__ import annotations
import math
from dataclasses import dataclass
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import AutoModel, AutoConfig
from transformers.modeling_utils import PreTrainedModel
from transformers.modeling_outputs import ModelOutput
# Relative import — HF's `trust_remote_code` loader bundles sibling .py
# files together and resolves these without the symbol being "installed".
from .configuration_marks import MarksConfig, TOPICS, TONES
# ---------------------------------------------------------------------------
# YaRN RoPE extension — per-dim ramp; applied after model load
# ---------------------------------------------------------------------------
def _yarn_inv_freq(
head_dim: int,
base: float,
scale: float,
original_max_position: int,
beta_fast: float = 32.0,
beta_slow: float = 1.0,
device=None,
dtype=torch.float32,
) -> torch.Tensor:
if scale <= 1.0:
return 1.0 / (base ** (torch.arange(0, head_dim, 2, device=device, dtype=dtype) / head_dim))
inv_freq_extrap = 1.0 / (base ** (torch.arange(0, head_dim, 2, device=device, dtype=dtype) / head_dim))
inv_freq_interp = inv_freq_extrap / scale
wavelengths = 2.0 * math.pi / inv_freq_extrap
L = original_max_position
ramp = (L / wavelengths - beta_slow) / (beta_fast - beta_slow)
ramp = ramp.clamp(0.0, 1.0)
return inv_freq_interp * (1.0 - ramp) + inv_freq_extrap * ramp
def _apply_yarn_to_modernbert(encoder, new_max_position: int,
original_max_position: int = 8192,
beta_fast: float = 32.0, beta_slow: float = 1.0):
if new_max_position == original_max_position:
return
scale = new_max_position / original_max_position
cfg = encoder.config
head_dim = cfg.hidden_size // cfg.num_attention_heads
global_base = float(getattr(cfg, "global_rope_theta", getattr(cfg, "rope_theta", 10000.0)))
rotary_modules = [
m for _, m in encoder.named_modules()
if m.__class__.__name__ == "ModernBertRotaryEmbedding"
]
for mod in rotary_modules:
full_buf = getattr(mod, "full_attention_inv_freq", None)
if full_buf is None or full_buf.numel() != head_dim // 2:
continue
new_inv = _yarn_inv_freq(
head_dim=head_dim, base=global_base, scale=scale,
original_max_position=original_max_position,
beta_fast=beta_fast, beta_slow=beta_slow,
device=full_buf.device, dtype=full_buf.dtype,
)
full_buf.data.copy_(new_inv)
# ---------------------------------------------------------------------------
# Output dataclass
# ---------------------------------------------------------------------------
@dataclass
class MarksOutput(ModelOutput):
loss: Optional[torch.Tensor] = None
loss_components: Optional[dict] = None
topic_mentioned_logits: Optional[torch.Tensor] = None # (B, 10)
topic_score: Optional[torch.Tensor] = None # (B, 10)
tone_score: Optional[torch.Tensor] = None # (B, 3)
# ---------------------------------------------------------------------------
# Model
# ---------------------------------------------------------------------------
class MarksMultiHead(PreTrainedModel):
"""Multi-head ModernBERT-large fine-tuned for earnings-call NLP scoring.
23 outputs per call:
* topic_mentioned (binary, 10 dims)
* topic_score (regression in [-2, +2], 10 dims)
* tone_score (regression in [1, 5], 3 dims)
"""
config_class = MarksConfig
base_model_prefix = "encoder"
supports_gradient_checkpointing = True
def __init__(self, config: MarksConfig):
super().__init__(config)
self.n_topics = len(config.topics)
self.n_tones = len(config.tones)
# Encoder — built from config (so we don't redownload base weights;
# weights come from this repo's safetensors).
if config.encoder_config:
enc_cfg = AutoConfig.from_dict(config.encoder_config) if hasattr(AutoConfig, "from_dict") else AutoConfig.for_model(**config.encoder_config)
else:
enc_cfg = AutoConfig.from_pretrained(config.encoder_name_or_path)
# Override the encoder ctx to the trained value (16384 for our v1).
enc_cfg.max_position_embeddings = config.max_position_embeddings
# Initialize encoder with config-only constructor (random init); the
# PreTrainedModel.from_pretrained caller will restore real weights
# from this repo's safetensors.
self.encoder = AutoModel.from_config(enc_cfg)
H = enc_cfg.hidden_size
# Head input is CLS + mean pool concatenated → 2H.
head_in = 2 * H
head_hidden = H // config.head_dim_ratio
def _mlp(out_dim: int) -> nn.Sequential:
return nn.Sequential(
nn.Linear(head_in, head_hidden),
nn.GELU(),
nn.Dropout(config.dropout),
nn.Linear(head_hidden, out_dim),
)
self.dropout = nn.Dropout(config.dropout)
self.head_topic_mentioned = _mlp(self.n_topics)
self.head_topic_score = _mlp(self.n_topics)
self.head_tone_score = _mlp(self.n_tones)
# Loss weights (used only if labels are passed for fine-tuning).
self._loss_weights = config.loss_weights
# Apply YaRN to encoder (idempotent if max_position == native).
if config.marks_rope_strategy == "yarn":
_apply_yarn_to_modernbert(
self.encoder,
new_max_position=config.max_position_embeddings,
original_max_position=config.original_max_position,
)
# NTK is applied inside encoder config; nothing to do here.
self.post_init()
# -------------------------------------------------------------------------
# Forward
# -------------------------------------------------------------------------
def forward(
self,
input_ids: torch.Tensor,
attention_mask: torch.Tensor,
topic_mentioned: Optional[torch.Tensor] = None,
topic_score: Optional[torch.Tensor] = None,
tone_score: Optional[torch.Tensor] = None,
**kwargs,
) -> MarksOutput:
out = self.encoder(input_ids=input_ids, attention_mask=attention_mask)
last_hidden = out.last_hidden_state # (B, T, H)
cls = last_hidden[:, 0] # (B, H)
m = attention_mask.unsqueeze(-1).to(last_hidden.dtype)
mean_pool = (last_hidden * m).sum(1) / m.sum(1).clamp(min=1.0) # (B, H)
pooled = self.dropout(torch.cat([cls, mean_pool], dim=-1)) # (B, 2H)
tm_logits = self.head_topic_mentioned(pooled)
ts_pred = self.head_topic_score(pooled)
tn_pred = self.head_tone_score(pooled)
loss, components = None, {}
if topic_mentioned is not None:
tm_logits_fp = tm_logits.float()
ts_pred_fp = ts_pred.float()
tn_pred_fp = tn_pred.float()
tm_t = topic_mentioned.float()
ts_t = topic_score.float()
tn_t = tone_score.float()
l_tm = F.binary_cross_entropy_with_logits(tm_logits_fp, tm_t)
l_ts = F.mse_loss(ts_pred_fp, ts_t)
l_tn = F.mse_loss(tn_pred_fp, tn_t)
components = {
"topic_mentioned": l_tm.detach(),
"topic_score": l_ts.detach(),
"tone_scores": l_tn.detach(),
}
w = self._loss_weights
loss = (
w["topic_mentioned"] * l_tm
+ w["topic_score"] * l_ts
+ w["tone_scores"] * l_tn
)
return MarksOutput(
loss=loss,
loss_components=components or None,
topic_mentioned_logits=tm_logits,
topic_score=ts_pred,
tone_score=tn_pred,
)
# -------------------------------------------------------------------------
# Convenience predict
# -------------------------------------------------------------------------
@torch.no_grad()
def predict(
self,
input_ids: torch.Tensor,
attention_mask: torch.Tensor,
mention_threshold: float = 0.5,
) -> dict:
"""Run a forward pass and return clamped + masked predictions.
Returns a dict with:
topic_mentioned (B, 10) hard 0/1
topic_mentioned_prob (B, 10) sigmoid confidence
topic_score (B, 10) clamped to [-2, +2], zeroed where mentioned=0
tone_score (B, 3) clamped to [1, 5]
"""
out = self.forward(input_ids=input_ids, attention_mask=attention_mask)
prob = torch.sigmoid(out.topic_mentioned_logits)
mentioned = (prob >= mention_threshold).float()
ts_lo, ts_hi = self.config.topic_score_range
tn_lo, tn_hi = self.config.tone_score_range
ts = out.topic_score.clamp(ts_lo, ts_hi) * mentioned
tn = out.tone_score.clamp(tn_lo, tn_hi)
return {
"topic_mentioned": mentioned,
"topic_mentioned_prob": prob,
"topic_score": ts,
"tone_score": tn,
}
# -------------------------------------------------------------------------
# Gradient checkpointing — delegate to encoder
# -------------------------------------------------------------------------
def gradient_checkpointing_enable(self, gradient_checkpointing_kwargs=None):
if hasattr(self.encoder, "gradient_checkpointing_enable"):
self.encoder.gradient_checkpointing_enable(
gradient_checkpointing_kwargs=gradient_checkpointing_kwargs
)
def gradient_checkpointing_disable(self):
if hasattr(self.encoder, "gradient_checkpointing_disable"):
self.encoder.gradient_checkpointing_disable()
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